WO2025136640A1

WO2025136640A1 - Systems and methods for wavelet transformation based dicrotic notch extraction and arrhythmia detection

Info

Publication number: WO2025136640A1
Application number: PCT/US2024/058220
Authority: WO
Inventors: Vahid SADRI; Michael Fonseca
Original assignee: TC1 LLC
Current assignee: TC1 LLC
Priority date: 2023-12-22
Filing date: 2024-12-03
Publication date: 2025-06-26
Anticipated expiration: 2026-06-22

Abstract

System and method for sensing an arterial pressure (AP) signal utilizing an implantable AP pressure sensor, the AP signal representative of variations in AP occurring during individual cardiac cycles within the patient. A wavelet transformation is applied to the AP signal to decompose the AP signal into frequency component (FC) bins. A subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest is selected, and the subset of FC bins are reconstructed to form a filtered AP signal. The dicrotic notch events are detected along the filtered AP signal, and a hemodynamic condition of the patient is monitored based on the dicrotic notch events.

Description

SYSTEMS AND METHODS FOR WAVELET TRANSFORMATION BASED DICROTIC NOTCH EXTRACTION AND ARRHYTHMIA DETECTION

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to United States Provisional

Application No. 63/613,972, titled “System and Method for Wavelet Transformation Based Dicrotic Notch Extraction and Arrhythmia Detection”, which was filed on December 22, 2023, and to United States Provisional Application No. 63/638,155, titled “Systems and Methods for Wavelet Transformation Based Dicrotic Notch Extraction and Arrhythmia Detection”, which was filed on April 24, 2024, the complete subject matter of which are expressly incorporated herein by reference in their entireties.

BACKGROUND

[0002] Embodiments of the present disclosure generally relate to implantable medical devices and methods, and more particularly to identifying features such as the dicrotic notch within a detected pressure waveform, as well as abnormal/arrhythmic heartbeats of a patient.

[0003] Cardiac signal analysis is vital for diagnosing and treating various cardiovascular diseases, such as arrhythmias and heart failure. In some cases, cardiac data can be acquired using an implantable medical device, such as a pressure sensor, that is implanted in the distal pulmonary artery and used in the treatment of heart failure (HF) patients. One example of an implantable pressure sensor is a passive pulmonary arterial (PA) pressure sensor, or passive PAP sensor. In general, a patient actively participates, such as daily or other periodic time period, to collect the physiologically relevant data and to make the data available to a clinician. For example, passive PA pressure sensors utilize an external device, outside of the patient body, for supplying energy to the sensors to power the generation and communication of the physiological data. The data may also be collected from the passive PAP sensor while the patient is in a clinical setting. The physiologic data provides information about the hemodynamic status of the patient and helps guide their treatment.

[0004] The PAP waveform contains various features that reflect different aspects of the cardiac cycle and the interaction between the heart and the pulmonary circulation. One of these features is the dicrotic notch, which is a small downward deflection in the arterial pressure waveform that occurs during diastole, shortly after the closure of the aortic/pulmonary valve. For example, if the measurement is taken by the pressure sensor implanted in the pulmonary artery, the dicrotic notch occurs shortly after the closure of the pulmonary valve, and if the measurement is instead taken by a pressure sensor in the aorta, the dicrotic notch occurs shortly after the closure of the aortic valve. The dicrotic notch marks the end of systole and the beginning of diastole, and it indicates the balance between the forward and backward waves in the pulmonary artery. The detection of the dicrotic notch can provide valuable information about the cardiac output, the systemic vascular resistance, the arterial compliance, and the presence of any valvular or vascular diseases. Therefore, accurate and continuous detection of the dicrotic notch is essential for the management and prognosis of HF patients.

[0005] The current method for detecting the dicrotic notch depends on the accuracy of the predicted notch time, which is based on an empirical equation that may not be valid for all patients or situations. Also, the current method cannot handle different types of arterial pressure signals that may not have a clear dicrotic notch due to noise or physiological variations. Further, the current method does not have the capability to detect abnormal/arrhythmic heartbeats reliably and accurately, especially in patients with HF or other cardiovascular diseases.

[0006] A need remains for a system and method to analyze arterial pressure signals that can overcome these limitations and provide a reliable way to monitor the cardiac function and hemodynamics of patients with HF or other cardiovascular diseases, and to detect any abnormal/arrhythmic heartbeats that may indicate a worsening of the patient’s condition or a need for medical attention.

SUMMARY

[0007] In accordance with embodiments herein, a method for use with an implantable arterial pressure sensor comprises sensing an arterial pressure (AP) signal utilizing the AP sensor, the AP signal representative of variations in AP occurring during individual cardiac cycles within the patient. The method includes applying a wavelet transformation to the AP signal to decompose the AP signal into frequency component (FC) bins, selecting a subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest, reconstructing the subset of FC bins to form a filtered AP signal, detecting dicrotic notch events along the filtered AP signal, and monitoring a hemodynamic condition of the patient based on the dicrotic notch events.

[0008] Optionally, the FC bins separate frequency components that correspond to different aspects of the cardiac cycle and different interactions between the heart and pulmonary circulation.

[0009] Optionally, the subset of the FC bins selected includes frequency components associated with movement of at least one of the aortic or pulmonary valves.

[0010] Optionally, the method further comprises calculating a normalized variability based on the dicrotic notch events, and determining that an arrythmia is associated with the AP signal when the normalized variability exceeds a threshold. [0011] Optionally, each of the FC bins includes an AP signal for a select FC over a select time frame.

[0012] Optionally, the wavelet transformation is a time domain based transformation. [0013] Optionally, the method further comprises calculating an AP derivative signal of the AP signal, deconstructing the AP derivative signal into derivative FC bins, selecting a second subset of FC bins from the derivative FC bins, and reconstructing a derivative dicrotic notch signal based on the second subset of FC bins, wherein the detecting the dicrotic notch events is based in part on the derivative dicrotic notch signal.

[0014] Optionally, the method further comprises identifying start and end events along the AP signal based on peaks of at least one of negative or positive segments of i) the filtered AP signal and ii) the derivative dicrotic notch signal reconstructed from the second subset of FC bins.

[0015] Optionally, the identifying the start and end events further comprises applying a threshold function to the filtered AP signal to create a pseudo systolic pressure signal, the threshold function applying a first value to positive elements of the filtered AP signal and a second value to negative elements of the filtered AP signal, identifying peaks of the positive segment of the pseudo systolic pressure signal, and identifying peaks of the negative segment of the derivative dicrotic notch signal, the start and end events identified based on the peaks of the positive segment of the pseudo systolic pressure signal and the negative segments of the derivative dicrotic notch signal.

[0016] Optionally, the method further comprising applying at least one of linear or nonlinear detrending to the AP signal to remove at least one of bias, drift, baseline wandering or high frequency noise.

[0017] In accordance with embodiments herein, a system for determining dicrotic notch events within an arterial pressure signal comprises an external device and an implantable pressure sensor (IPS). The IPS comprises an IPS sensing circuit configured to sense pressure for a period of time, and to generate a pressure signal based on the pressure, and an IPS communications circuit configured to communicate with the external device. At least one of the IPS or external device further comprises memory configured to store program instructions, and one or more processors that, when executing the program instructions, are configured to sense an arterial pressure (AP) signal utilizing the IPS sensor, the AP signal representative of variations in AP occurring during individual cardiac cycles within the patient, apply a wavelet transformation to the AP signal to decompose the AP signal into frequency component (FC) bins, select a subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest, reconstruct the subset of FC bins to form a filtered AP signal, detect dicrotic notch events along the filtered AP signal, and monitor a hemodynamic condition of the patient based on the dicrotic notch events.

[0018] Optionally, the FC bins separate frequency components that correspond to different aspects of the cardiac cycle and different interactions between the heart and pulmonary circulation.

[0019] Optionally, the subset of the FC bins selected includes frequency components associated with movement of at least one of the aortic or pulmonary valves.

[0020] Optionally, the wavelet transformation is a time domain based transformation.

[0021] Optionally, the one or more processors are further configured to calculate a normalized variability based on the dicrotic notch events, and determine that an arrythmia is associated with the AP signal when the normalized variability exceeds a threshold.

[0022] Optionally, the normalized variability is based on amplitudes of the dicrotic notch events and time between adjacent dicrotic notch events.

[0023] Optionally, the one or more processors are further configured to calculate an AP derivative signal of the AP signal, deconstruct the AP derivative signal into derivative FC bins, select a second subset of FC bins from the derivative FC bins, and reconstruct a derivative dicrotic notch signal based on the second subset of FC bins, wherein the detecting the dicrotic notch events is based in part on the derivative dicrotic notch signal.

[0024] Optionally, the one or more processors are further configured to identify start and end events along the AP signal based on peaks of at least one of negative or positive segments of i) the filtered AP signal and ii) the derivative dicrotic notch signal reconstructed from the second subset of FC bins.

[0025] Optionally, the one or more processors are further configured to apply a threshold function to the filtered AP signal to create a pseudo systolic pressure signal, the threshold function applying a first value to positive elements of the filtered AP signal and a second value to negative elements of the filtered AP signal, identify peaks of the positive segment of the pseudo systolic pressure signal, and identify peaks of the negative segment of the derivative dicrotic notch signal, the start and end events identified based on the peaks of the positive segment of the pseudo systolic pressure signal and the negative segments of the derivative dicrotic notch signal.

[0026] Optionally, the one or more processors are further configured to apply at least one of linear or nonlinear detrending to the AP signal to remove at least one of bias, drift, baseline wandering or high frequency noise.

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 illustrates a system that includes an implantable medical device (IMD), an implantable pressure sensor (IPS), and an external device implemented in accordance with embodiments herein.

[0028] Figure 2 illustrates a block diagram of the system formed in accordance with embodiments herein, showing some of the components of the IPS, IMD, external device, and wearable device.

[0029] Figures 3A and 3B illustrate a computer-implemented method for implementing a dicrotic notch detection algorithm for detecting the location of the dicrotic notch in cardiac pressure signals in accordance with embodiments herein. [0030] Figure 4A shows a graph of a pulmonary pressure waveform based on an arterial pressure signal (e.g., AP signal) that has been acquired by the IPS over time in accordance with embodiments herein.

[0031] Figure 4B shows a graph of linear detrended waveform, based on the pressure waveform of Figure 4A, in accordance with embodiments herein.

[0032] Figures 4C and 4D show visual examples of binary filters extracting a dicrotic notch search area from the filtered AP signal in accordance with embodiments herein.

[0033] Figure 4E is an example of an original AP signal waveform, similar to Figure 4A, that is based on a pressure signal that has been acquired by the IPS over time in accordance with embodiments herein.

[0034] Figure 4F is an example of a detrended AP derivative waveform based on the AP signal waveform of Figure 4E in accordance with embodiments herein.

[0035] Figure 4G illustrates a reconstructed derivative dicrotic notch signal that is based on the detrended AP derivative in accordance with embodiments herein.

[0036] Figure 4H illustrates a squared reconstructed signal based on the derivative dicrotic notch signal in accordance with embodiments herein.

[0037] Figure 4I illustrates peaks of the squared reconstructed signal in accordance with embodiments herein.

[0038] Figure 4J illustrates the timing of start and end events of a dicrotic notch search area based on the peaks identified in Figure 4I in accordance with embodiments herein.

[0039] Figure 4K illustrates the result of filtering the dicrotic notch search area with a final binary filter in accordance with embodiments herein.

[0040] Figure 4L shows the AP signal waveform and illustrates the locations of peak systole, peak diastole, and the dicrotic notch on the cardiac cycles in accordance with embodiments herein. [0041] Figure 5 illustrates a method for determining abnormal/arrhythmic heartbeats based on the dicrotic notch location in accordance with embodiments herein.

[0042] Figure 6A illustrates an original AP signal waveform with peak systole, peak diastole, and the dicrotic notch indicated in accordance with embodiments herein, wherein the AP signal reflects a relatively regular heartbeat. [0043] Figure 6B illustrates a waveform of a corresponding final filtered signal, which isolates the dicrotic notch feature from the arterial pulse wave in accordance with embodiments herein.

[0044] Figure 7A illustrates another original AP signal with peak systole, peak diastole, and the dicrotic notch indicated in accordance with embodiments herein, wherein the AP signal reflects a relatively irregular heartbeat.

[0045] Figure 7B illustrates a waveform of a corresponding final filtered signal, which isolates the dicrotic notch feature from the arterial pulse wave in accordance with embodiments herein.

[0046] Figure 8 illustrates a multiresolution analysis based on wavelet analysis in accordance with embodiments herein.

[0047] Figure 9 illustrates exemplary maximal overlap discrete wavelet transform (MODWT) levels that each correspond to a different frequency band in accordance with embodiments herein.

[0048] Figure 10 shows an example block diagram of an IMD formed in accordance with embodiments herein.

[0049] Figure 11 illustrates a digital healthcare system implemented in accordance with embodiments herein.

[0050] Figure 12 illustrates a computer-implemented method for detecting the location of the dicrotic notch in cardiac pressure signals in accordance with embodiments herein. DETAILED DESCRIPTION

[0051] It will be readily understood that the components of the embodiments as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations in addition to the described example embodiments. Thus, the following more detailed description of the example embodiments, as represented in the figures, is not intended to limit the scope of the embodiments, as claimed, but is merely representative of example embodiments.

[0052] Reference throughout this specification to “one embodiment” or “an embodiment” (or the like) means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment” or “in an embodiment” or the like in various places throughout this specification are not necessarily all referring to the same embodiment.

[0053] Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that the various embodiments can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obfuscation. The following description is intended only by way of example, and simply illustrates certain example embodiments.

[0054] The methods described herein may employ structures or aspects of various embodiments (e.g., systems and/or methods) discussed herein. In various embodiments, certain operations may be omitted or added, certain operations may be combined, certain operations may be performed simultaneously, certain operations may be performed concurrently, certain operations may be split into multiple operations, certain operations may be performed in a different order, or certain operations or series of operations may be re-performed in an iterative fashion. It should be noted that, other methods may be used, in accordance with an embodiment herein. Further, wherein indicated, the methods may be fully or partially implemented by one or more processors of one or more devices or systems. While the operations of some methods may be described as performed by the processor(s) of one device, additionally, some or all of such operations may be performed by the processor(s) of another device described herein.

[0055] Embodiments may be implemented in connection with one or more implantable medical devices (IMDs). Non-limiting examples of IMDs include one or more of implantable leadless monitoring and/or therapy devices, and/or alternative implantable medical devices. For example, the IMD may represent a cardiac monitoring device, cardioverter defibrillator, pacemaker, cardiac rhythm management device, leadless pacemaker, leadless implantable medical device (LIMD), and the like.

[0056] Additionally or alternatively, the IMD may be a subcutaneous IMD that includes one or more structural and/or functional aspects of the device(s) described in U.S. Patent 10,765,860, titled “Subcutaneous Implantation Medical Device With Multiple Parasternal-Anterior Electrodes”; U.S. Patent 10,722,704, titled “Implantable Medical Systems And Methods Including Pulse Generators And Leads”; US Patent 11 ,045,643, titled “Single Site Implantation Methods For Medical Devices Having Multiple Leads”, the complete subject matter of which are hereby incorporated by reference in their entireties. Further, one or more combinations of IMDs may be utilized from the incorporated patents and applications identified herein in accordance with embodiments herein.

[0057] In accordance with embodiments herein, the methods, devices, and systems may be implemented in connection with the systems and methods described in U.S. published application US20210020294A1 , entitled “METHODS DEVICE AND SYSTEMS FOR HOLISTIC INTEGRATED HEALTHCARE PATIENT MANAGEMENT” filed July 16, 2020, and U.S. patent application 18/325,147, filed on May 30, 2023, titled “System and Method for Inter-Device Arrhythmia Detection and Confirmation”, which are incorporated herein by reference in their entirety. In accordance with embodiments herein, the methods, devices, and systems may be implemented in connection with the communications systems and methods described in U.S. patent application 17/820,654, filed on August 18, 2022, titled “System and Method for Intra-Body Communication of Sensed Physiologic Data”, which is incorporated herein by reference in its entirety. In accordance with embodiments herein, the methods, devices, and systems may be implemented in connection with those described in US patent 11 ,559,241 , filed on October 01 , 2019, titled “Methods and Systems for Reducing False Declarations of Arrythmias”, which is incorporated herein by reference in its entirety.

[0058] The complete subject matter of all references, including publications, patent applications and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Terms

[0059] The term “dicrotic notch” is used to refer to a small deflection in the pressure waveform of the central arteries (e.g., pulmonary, aortic) that occurs during diastole, shortly after the closure of the aortic/pulmonary valve. The dicrotic notch marks the end of systole and the beginning of diastole. In some cases, the dicrotic notch refers to a small downward deflection.

[0060] The term “dicrotic notch event” is used to refer to a location of the dicrotic notch, as well as start events and end events that define a dicrotic notch search area having a range of time within a cardiac cycle during which the dicrotic notch is expected to occur.

[0061] The terms “valid” and “normal” are used interchangeably to refer to events, features, and characteristics of, or appropriate to, a healthy functioning of the heart. [0062] The terms “invalid”, “abnormal”, “arrhythmic”, and “arrhythmia” are used interchangeably to refer to events, features, and characteristics of, or appropriate to, an unhealthy functioning of the heart.

[0063] The terms “cardiac activity signal”, “cardiac activity signals”, “CA signal” and “CA signals” (collectively “CA signals”) are used interchangeably throughout to refer to measured signals indicative of cardiac activity by a region or chamber of interest. For example, the CA signals may be indicative of impedance, electrical or mechanical activity by one or more chambers (e.g., left or right ventricle, left or right atrium) of the heart and/or by a local region within the heart (e.g., impedance, electrical or mechanical activity at the AV node, along the septal wall, within the left or right bundle branch, within the purkinje fibers). The cardiac activity may be normal/healthy or abnormal/arrhythmic. Examples of CA signals includes electrogram (EGM) signals and intracardiac electrogram (IEGM) signals. Electrical based CA signals refer to an analog or digital electrical signal recorded by two or more electrodes, where the electrical signals are indicative of cardiac activity. Heart sound (HS) based CA signals refer to signals output by a heart sound sensor such as an accelerometer, where the HS based CA signals are indicative of one or more of the S1 , S2, S3 and/or S4 heart sounds. Impedance based CA signals refer to impedance measurements recorded along an impedance vector between two or more electrodes, where the impedance measurements are indicative of cardiac activity.

[0064] The term “PA” shall mean pulmonary artery. The term “PAP” shall mean pulmonary arterial pressure.

[0065] The term “AP” shall mean arterial pressure and may refer to pulmonary arterial pressure or aortic pressure.

[0066] The term “pressure signal” shall refer to measured signals indicative of blood flow pressure within the body. One example is pulmonary arterial pressure that is measured within the pulmonary artery. Another example is aortic pressure that is measured within the aortic artery. [0067] The term “signal” shall mean pressure signal, pulmonary signal, aortic signal, physiological signal, hemodynamic signal, periodic signal, arterial line signal, capillary blood flow signal, blood flow signal based on a sensed hemodynamic signal, and/or blood flow signal based on a sensed physiological signal.

[0068] The term “pseudo-systolic” shall refer to waveforms based on signals that have undergone some level of signal processing and are representative of potential systolic segments of a heartbeat.

[0069] The term “POC” shall mean point-of-care. The terms “point-of-care” and “POC”, when used in connection with medical diagnostic testing, shall mean methods and devices configured to provide medical diagnostic testing at or near a time and place of patient care. The time and place of patient care may be at an individual’s home, such as when providing “at home” point of care solutions. The time and place of patient care may be at a physician’s office or other medical facility, wherein one or more medical diagnostic tests may be performed on-site at a time of or shortly after a patient visit and collection of a patient sample. The POC may implement the methods, devices and systems described in one or more of the following publications, all of which are expressly incorporated herein by reference in their entireties: U.S. Patent Number 6,786,874, entitled “APPARATUS AND METHOD FOR THE COLLECTION OF INTERSTITIAL FLUIDS” issued September 7, 2004; U.S. Patent Number 9,494,578, entitled “SPATIAL ORIENTATION DETERMINATION IN PORTABLE CLINICAL ANALYSIS SYSTEMS” issued November 15, 2016; and U.S. Patent Number 9,872,641 , entitled “METHODS, DEVICES AND SYSTEMS RELATED TO ANALYTE MONITORING” issued January 23, 2018.

[0070] The term “obtains”, “obtaining”, “collect”, and “collecting”, as used in connection with data, signals, information and the like, can be used interchangeably herein and include at least one of i) accessing memory of an external device or remote server where the data, signals, information, etc., are stored, ii) receiving the data, signals, information, etc., over a wireless communications link between the IMD and a local external device, and/or iii) receiving the data, signals, information, etc., at a remote server over a network connection. The obtaining operation, when from the perspective of an IMD and/or implantable sensor, may include sensing new signals in real time, and/or accessing memory to read stored data, signals, information, etc., from memory within the IMD. The obtaining operation, when from the perspective of a local external device, includes receiving the data, signals, information, etc., at a transceiver of the local external device where the data, signals, information, etc., are transmitted from an IMD and/or a remote server. The obtaining operation may be from the perspective of a remote server, such as when receiving the data, signals, information, etc., at a network interface from a local external device and/or directly from an IMD. The remote server may also obtain the data, signals, information, etc., from local memory and/or from other memory, such as within a cloud storage environment and/or from the memory of a workstation or clinician external programmer. The IMD and implantable sensor may also obtain data, signals, and information from each other in response to a request or a triggering event.

[0071] The terms “processor,” “a processor”, “one or more processors” and “the processor” shall mean one or more processors. The one or more processors may be implemented by one, or by a combination of more than one implantable medical device, a wearable device, a local device, a remote device, a server computing device, a network of server computing devices and the like. The one or more processors may be implemented at a common location or at distributed locations. The one or more processors may implement the various operations described herein in a serial or parallel manner, in a shared-resource configuration and the like.

[0072] The term “health care system” refers to a system that includes equipment for measuring health parameters, and communication pathways from the equipment to secondary devices. The secondary devices may be at the same location as the equipment, or remote from the equipment at a different location. The communication pathways may be internal within the patient, wired, wireless, over the air, cellular, in the cloud, etc. In one example, the healthcare system provided may be one of the systems described in U.S. published application US20210020294A1 , entitled “METHODS DEVICE AND SYSTEMS FOR HOLISTIC INTEGRATED HEALTHCARE PATIENT MANAGEMENT” filed July 16, 2020, which is incorporated herein by reference in its entirety. Other patents that describe example monitoring systems include U.S. Pat. No. 6,572,557; entitled SYSTEM AND METHOD FOR MONITORING PROGRESSION OF CARDIAC DISEASE STATE USING PHYSIOLOGIC SENSORS, filed Dec. 21 , 2000, to Tchou et al.; U.S. Pat. No. 6,480,733 entitled METHOD FORMONITORING HEART FAILURE filed Dec. 17, 1999, to Turcott; U.S. Pat. No. 7,272,443 entitled SYSTEM AND METHOD FOR PREDICTING A HEART CONDITION BASED ON IMPEDANCE VALUES USING AN IMPLANTABLE MEDICAL DEVICE, filed Dec. 14, 2004, to Min et al; U.S. Pat. No. 7,308,309 entitled DIAGNOSING CARDIAC HEALTH UTILIZING PARAMETER TREND ANALYSIS, filed Jan. 11 , 2005, to Koh; and U.S. Pat. No. 6,645,153 entitled SYSTEM AND METHOD FOR EVALUATING RISK OF MORTALITY DUE TO CONGESTIVE HEART FAILURE USING PHYSIOLOGIC SENSORS, filed Feb. 7, 2002, to Kroll et. al., the entire contents of which are incorporated in full herein by reference.

[0073] The term “real-time” shall mean a time frame contemporaneous with normal or abnormal episode occurrences. For example, a real-time process or operation would occur during or immediately after (e.g., within seconds after) a cardiac event, a series of cardiac events, an arrhythmia episode, and the like. For example, the term “real-time” may refer to a time period substantially contemporaneous with an event of interest. The term “real-time,” when used in connection with collecting and/or processing data utilizing an IMD or IPS, shall refer to processing operations performed substantially contemporaneous with a physiologic event of interest experienced by a patient, such as an arrhythmia, the closing/opening of a cardiac valve, detection of dicrotic notch, and the like. By way of example, in accordance with embodiments herein, pressure and/or cardiac activity signals can be analyzed in real time (e.g., during a cardiac event or within a few minutes after the cardiac event).

[0074] The term “on-demand” shall mean at any time that the system automatically determines that a measurement is warranted and without any need for patient action or intervention. As one example, an implantable sensor will collect pressure measurements “on-demand” automatically and in real-time in response to a data collection instruction from an IMD. As another example, an implantable sensor will collect pressure measurements “on-demand” automatically and in real-time in response to a data collection instruction from an external device such as a bedside monitor, smart phone, physician’s programmer and the like. As another example, an implantable sensor will collect pressure measurements “on- demand” automatically and in real-time in response to a data collection schedule stored at the sensor, IMD or external device.

[0075] Embodiments may be implemented in connection with one or more implantable medical devices (IMDs). Non-limiting examples of IMDs include one or more of neurostimulator devices, implantable leadless monitoring and/or therapy devices, and/or alternative implantable medical devices. For example, the IMD may represent a cardiac monitoring device, pacemaker, cardioverter, cardiac rhythm management device, defibrillator, neurostimulator, leadless monitoring device, leadless pacemaker and the like. For example, the IMD may include one or more structural and/or functional aspects of the device(s) described in U.S. Patent 9,333,351 “Neurostimulation Method And System To Treat Apnea” and U.S. Patent 9,044,610 “System And Methods For Providing A Distributed Virtual Stimulation Cathode For Use With An Implantable Neurostimulation System”, which are hereby incorporated by reference. [0076] Additionally or alternatively, the IMD may be a leadless implantable medical device (LIMD) that includes one or more structural and/or functional aspects of the device(s) described in U.S. Patent 9,216,285 “Leadless Implantable Medical Device Having Removable And Fixed Components” and U.S. Patent 8,831 ,747 “Leadless Neurostimulation Device And Method Including The Same”, which are hereby incorporated by reference. Additionally or alternatively, the IMD may include one or more structural and/or functional aspects of the device(s) described in U.S. Patent 8,391 ,980 “Method And System For Identifying A Potential Lead Failure In An Implantable Medical Device” and U.S. Patent 9,232,485 “System And Method For Selectively Communicating With An Implantable Medical Device”, which are hereby incorporated by reference in their entireties.

[0077] Additionally or alternatively, the IMD may be a subcutaneous IMD that includes one or more structural and/or functional aspects of the device(s) described in U.S. Patent Number 10,765,860, entitled “Subcutaneous Implantation Medical Device With Multiple Parasternal-Anterior Electrodes” issued September 08, 2020; U.S. Patent Number 10,722,704, entitled “Implantable Medical Systems And Methods Including Pulse Generators And Leads” issued July 28, 2020; and U.S. Patent Number 11 ,045,643, entitled “Single Site Implantation Methods For Medical Devices Having Multiple Leads”, issued June 29, 2021 , the complete subject matter of which are hereby incorporated by reference in their entireties. Further, one or more combinations of IMDs may be utilized from the above incorporated patents and applications in accordance with embodiments herein.

[0078] Additionally or alternatively, the IMD may be a leadless cardiac monitor (ICM) that includes one or more structural and/or functional aspects of the device(s) described in U.S. Patent 9,949,660, entitled “METHOD AND SYSTEM TO DISCRIMINATE RHYTHM PATTERNS IN CARDIAC ACTIVITY” issued April 24, 2018, which is expressly incorporated herein by reference in its entirety. [0079] The implantable medical sensor disclosed herein may implement one or more structural and/or functional aspects of the device(s) described in U.S. patent 11 ,033,192, filed November 16, 2018, and entitled “Wireless Sensor for Measuring Pressure”; U.S. patent 10,143,388, filed Jun. 8, 2015, titled "Method of Manufacturing Implantable Wireless Sensor for In Vivo Pressure Measurement”; U.S. patent 9,078,563, filed Nov. 4, 2009, titled "Method of Manufacturing Implantable Wireless Sensor for In Vivo Pressure Measurement"; U.S. patent 7,621 ,036, filed on Aug. 16, 2005, titled "Method of Manufacturing Implantable Wireless Sensor for In Vivo Pressure Measurement"; U.S. published patent application 2006/0287602, Ser. No. 11/157,375, filed Jun. 21 , 2005, titled "Implantable Wireless Sensor for In Vivo Pressure Measurement,"; and U.S. patent application Ser. No. 63/574,335, filed April 4, 2024, titled “Method and Device for Cardiac Pressure Sensing Using an Active Implantable Device and Near Field Communication,” which are expressly incorporated herein by reference in their entireties.

[0080] Embodiments may be implemented in connection with one or more PIMDs. Non-limiting examples of PIMDs may include passive wireless sensors used by themselves or incorporated into or used in conjunction with other implantable medical devices (IMDs) such as cardiac monitoring devices, pacemakers, cardioverters, cardiac rhythm management devices, defibrillators, neurostimulators, leadless monitoring devices, leadless pacemakers, replacement valves, shunts, grafts, drug elution devices, blood glucose monitoring systems, orthopedic implants, and the like. For example, the PIMD may include one or more structural and/or functional aspects of the device(s) described in U.S. Patent No. 9,265,428 entitled “Implantable Wireless Sensor”, U.S. Patent No. 8,278,941 entitled “Strain Monitoring System and Apparatus”, U.S. Patent No. 8,026,729 entitled “System and Apparatus for In-Vivo Assessment of Relative Position of an Implant”, U.S. Patent No. 8,870,787 entitled “Ventricular Shunt System and Method”, and U.S. Patent No. 9,653,926 entitled “Physical Property Sensor with Active Electronic Circuit and Wireless Power and Data Transmission”, which are all hereby incorporated by reference in their respective entireties.

[0081] The terms “arrhythmia treatment”, “in connection with treating a heart condition” and similar phrases, as used herein include, but are not limited to, delivering an electrical stimulation or drug therapy to a heart condition. By way of example, treating a heart condition may include, in whole or in part, i) identifying a progression of heart failure over time; ii) confirming an arrhythmia identified by an arrhythmia detection process; iii) instructing the patient to perform a posture recalibration procedure and/or iv) delivering a therapy.

[0082] The term “treatment notification” shall mean a communication and/or device command to be conveyed to one or more individuals and/or one or more other electronic devices, including but not limited to, network servers, workstations, laptop computers, tablet devices, smart phones, IMDs, electronic dispensing tool (EDT) equipment and the like. When a treatment notification is provided as a communication, the treatment notification may represent in an audio, video, vibratory or other user perceivable medium. The communication may be presented in various formats, such as to display patient information, messages, user directions and the like. The communication is presented on one or more of the various types of electronic devices described herein and may be directed to a patient, a physician, various medical personnel, various patient record management personnel and the like. The communication may represent an identification of a patient diagnosis and various treatment recommendations. The diagnosis and treatment recommendation may be provided directly to the patient. For example, in some circumstances, a diagnosis and treatment recommendation may be to modify a dosage level, in which case, the notification may be provided to the physician or medical practitioner. As another example, the diagnosis and treatment recommendation may be to begin, change or end certain physical activities, in which case, the notification may be provided to the patient, in addition to the physician or medical practitioner. As another example, the treatment notification may present an indication that a patient may or may not be a good candidate suited for implant of a ventricular assist device (e.g., LV assist device), a transplant, a valve repair procedure (e.g., a MitraClip™ valve repair to correct mitral regurgitation) and the like. Other nonlimiting examples of a communication type notification include, in part or in whole, a recommendation to schedule an appointment with a physician, schedule an appointment for additional blood work, perform an additional at home POC blood analysis (e.g., utilizing at home EDT equipment), recommend that the patient collect additional EDT and/or IMD data. When a notification includes an action that may be performed by a patient alone, the notification may be communicated directly to the patient. Other nonlimiting examples of a communication type notification include communications sent to a patient via an electronic device, where the communication informs the patient of how a patient’s lifestyle choices are directly affecting the patient’s health. For example, when a patient consumes too much sugar, a notification may be sent to the patient to inform that the excessive sugar has caused a spike in the patient’s glucose level. As another example, when a patient avoids exercise for a period of time, the notification may inform a patient that the patient’s lack of exercise has raised a PAP trend and/or introduced an undue burden on a patient’s kidneys.

[0083] When a treatment notification is provided as a device command, the treatment notification may represent an electronic command directing a computing device (e.g., IMD, EDT equipment, local external device, server) to perform an action. For example, the action may include directing the following:

1. IMD or EDT equipment to provide additional IMD data and/or EDT data already available;

2. IMD or EDT equipment to collect additional data and/or another type of data;

3. IMD to deliver a therapy and/or modify a prior therapy (e.g., a pacing therapy, neural stimulation therapy, appetite suppression therapy, drug delivery rate); 4. Local external device to provide additional information regarding past and present behavior of the patient; and

5. Server to analyze further information in the patient medical record and/or from another medical record.

[0084] The term “treatment recommendation” shall mean a recommendation for the patient, medical personnel and/or a device (e.g., an IMD, local external device, remote server, or BGA device) to take an action and/or maintain a current course of action. Non-limiting examples of treatment recommendations include dispatching an ambulance to the patient’s location, instructing the patient immediately go to a hospital, instructing the patient schedule an appointment, instructing the patient change a prescription, instructing the patient undergo additional examinations (e.g., diagnostic imaging examinations, exploratory surgery and the like), instructing the patient undergo a POC test to collect new BGA data, instructing the patient take a nutritional supplement, instructing the patient start, stop or change a physical activity, or instructing the patient make no changes. The treatment recommendation may include an instruction to change, maintain, add or stop a therapy delivered by an active IMD, such as a pacing therapy, and ATP pacing therapy, a neural stimulation therapy, mechanical circulatory support and the like.

[0085] The terms “treat” and “treatment”, when used in connection with a heart condition, shall mean to affect a particular treatment or prophylaxis for a heart disease or heart condition, including i) to prevent a particular heart disease or heart condition, or ii) to change (e.g., slow) progression of the particular heart disease or heart condition. By way of example, the treatment may constitute i) delivering a stimulation therapy or drug by an implantable medical device (IMD), a wearable medical device, or an external device, ii) changing in a stimulation parameter or drug regiment, iii) programming stimulation parameters of the IMD, iv) prescribing implant of an IMD, v) prescribing a drug delivery pump, and/or vi) implanting an IMD or drug delivery pump to treat a heart condition such as one or more of arrhythmia, abnormal heart beats, heart failure, and the like.

[0086] Additionally or alternatively, the processes, systems, components, etc., described herein may be implemented utilizing all or portions of the structural and/or functional aspects of the methods and systems described in US published application number 2014/0330143, filed May 2, 2014, titled “Method and system for treating cardiovascular disease”; US published application number 2014/0288459, filed March 25, 2013, titled “Ventricular shunt system and method”; US published application number 2014/0288085, filed March 17, 2014, titled “Methods for the Treatment of Cardiovascular Conditions"; US published application number 2014/0275861 , filed March 17, 2014, titled “Ambulatory sensing system and associated methods”; US published application number 2014/0155769, filed November 21 , 2013, titled “Devices, Systems, and Methods for Pulmonary Arterial Hypertension (PAH) Assessment and Treatment”; US published application number 2014/0084943, filed September 21 , 2012, titled “Strain monitoring system and apparatus; US published application number 2014/0088994, filed September 23, 2013, titled “Method and system for trendbased patient management”; US published application number 2013/0245469, filed March 15, 2013, titled “Pulmonary Arterial Hemodynamic Monitoring for Chronic Obstructive Pulmonary Disease Assessment and Treatment”; US published application number 2015/0133796, filed November 6, 2014, titled “Systems and methods for using physiological information”; US patent 8,669,770, filed November 15, 2010, titled “Selectively actuating wireless, passive implantable sensor”; US published application number 2013/0296721 , January 29, 2013, titled “Hypertension System And Method”; US patent 8,264,240, July 20, 2009, titled “Physical property sensor with active electronic circuit and wireless power and data transmission”; US patent 8,159,348, filed February 26, 2009, titled “Communication system with antenna box amplifier”; US patent 7 ,667,547 , filed August 22, 2007, titled “Loosely-coupled oscillator”; US patent 7,966,886, filed October 9, 2009, titled “Method and apparatus for measuring pressure inside a fluid system”; US patent 8,665,086, January 4, 2012, titled “Physiological data acquisition and management system for use with an implanted wireless sensor”; US patent 7,908,018, September 6, 2006, titled “Flexible electrode”; US patent 7,909,770, July 3, 2007, titled “Method for using a wireless pressure sensor to monitor pressure inside the human heart”; US patent 7,812,416, filed May 15, 2007, titled “Methods and apparatus having an integrated circuit attached to fused silica”; US patent 7,829,363, May 10, 2007, titled “Method and apparatus for microjoining dissimilar materials”; US published application number 2007/0199385, filed November 17, 2006, titled “Capacitor electrode formed on surface of integrated circuit chip”; US patent 7 ,748,277 , filed October 18, 2006, titled “Hermetic chamber with electrical feedthroughs”; US published application number 2007/0158769, filed October 12, 2006, titled “Integrated CMOS-MEMS technology for wired implantable sensors”; US patent 7,710,103, filed January 7, 2009, titled “Preventing false locks in a system that communicates with an implanted wireless sensor”; US patent 8,896,324, filed September 26, 2011 , titled “System, apparatus, and method for in-vivo assessment of relative position of an implant”; US published application number 2012/0016207, filed September 26, 2011 , titled “Electromagnetically coupled hermetic chamber”; US patent 8,355,777, filed September 19, 2011 , titled “Apparatus and method for sensor deployment and fixation”; US patent 7,854,172, filed February 17, 2009, titled “Hermetic chamber with electrical feedthroughs”; US patent 7,147,604, filed August 7, 2002, titled “High Q factor sensor”; US patent 7,618,363, filed August 6, 2003, titled “Hydraulically actuated artificial muscle for ventricular assist”; US patent 7,699,059, filed January 22, 2002, titled “Implantable wireless sensor”; US patent 7,481 ,771 , filed July 7, 2007, titled “Implantable wireless sensor for pressure measurement within the heart”; US published application number 2022/0079456, filed October 21 , 2021 , titled “System and method for calculating a lumen pressure utilizing sensor calibration parameters”; PCT application number PCT/US24/52734, filed October 24, 2024, claiming priority to US serial number 63/596,402, filed November 6, 2023, titled “System and Method for Diastolic- Enhanced Systolic Peak Detection”; US patent 7,439,723, filed March 14, 2007, titled “Communicating with an implanted wireless sensor”; US patent 7,498,799, filed March 6, 2006, titled “Communicating with an implanted wireless sensor”; and US patent 11 ,832,920, filed June 5, 2020, titled “Devices, Systems, and Methods for Pulmonary Arterial Hypertension (PAH) Assessment and Treatment”, which are hereby incorporated by reference in their entireties.

[0087] Additionally or alternatively, one or more structural and/or functional aspects of the device(s) and/or method(s) described in US patent 9,301 ,702, filed November 19, 2012, titled “Systems and methods for exploiting pulmonary artery pressure obtained from an implantable sensor to detect cardiac rhythm irregularities”; US patent 9,566,442, filed November 19, 2012, titled “Systems and methods for using pulmonary artery pressure from an implantable sensor to detect mitral regurgitation and optimize pacing delays”; US patent 9,162,065, filed February 6, 2015, titled “Systems and methods for estimating central arterial blood pressure of a patient”; US patent 8,914,108, filed January 6, 2014, titled “Method for hemodynamic optimization using plethysmography”; US patent 9,687,656, filed December 14, 2009, titled “Arterial blood pressure monitoring devices, systems and methods for use while pacing”; US patent 8,428,698, filed May 29, 2009, titled “Systems and methods for monitoring DP, IVRT, DiFT, diastolic function and/or HF”; US patent 10,045,701 , filed November 6, 2021 , titled “Implantable hemodynamic monitor and methods for use therewith”; US patent 8,162,841 , filed May 29, 2009, titled “Standalone systemic arterial blood pressure monitoring device”; and US patent 6,575,912, filed October 16, 2001 , titled “Assessing heart failure status using morphology of a signal representative of arterial pulse pressure”, which are hereby incorporated by reference in their entireties. System Overview

[0088] In accordance with new and unique aspects herein, methods and devices are described for analyzing arterial pressure signals obtained by an implantable pressure device, such as an implantable pressure sensor. A waveletbased technique is used to extract portions of the signal that include the dicrotic notch. One or more threshold function, logical function and/or binary filter can be used to identify the location of the dicrotic notch in each cardiac cycle. One or more hemodynamic conditions of a patient can be monitored based on the dicrotic notch.

[0089] In some embodiments, the determined dicrotic notch locations can be used to detect abnormal/arrhythmic heartbeats and trigger an alert or notification to doctors or clinical staff.

[0090] The methods and devices described have several advantages over conventional methods for analyzing arterial pressure signals. For example, the accuracy and robustness of the analysis is improved by using a wavelet-based technique that can capture the high-frequency components of the arterial pressure, such as the dicrotic notch. The dicrotic notch is a point on the arterial pulse wave that indicates the closure of the heart valve, occurring shortly after the closure of the aortic/pulmonary valve. It is important for measuring the systolic and diastolic durations, which are indicators of cardiac function and hemodynamics. The methods and devices can also handle different types of arterial pressure signals that may not have a clear dicrotic notch due to noise or physiological variations. The methods and devices can also help diagnose and monitor various cardiac conditions and disorders that may cause abnormal/arrhythmic heartbeats, including but not limited to atrial fibrillation, ventricular tachycardia, and/or myocardial infarction. For example, abnormal/arrhythmic heartbeats can indicate a deterioration of the patient’s condition or a need for medical intervention.

[0091] In accordance with new and unique aspects herein, the methods and devices determine the dicrotic notch accurately in signals that include noise and/or arrhythmia. Therefore, a technical advantage is realized as an accurate heart rate is important both for accessing the immediate status of the patient, as well as to provide accurate data input that is used by other algorithms to assess the patient, treat the patient, modify treatment of the patient, provide a treatment notification, provide a treatment recommendation, select an appropriate therapy for the patient, reprogram a device such as an implantable medical device, implantable sensor, implantable pressure sensor, external device, etc., and/or display information and/or recommendations related to the valid heartbeats and/or detected changes and status of the patient.

[0092] Figure 1 illustrates a system 101 that includes an implantable medical device (IMD) 100, an implantable pressure sensor (IPS) 150, and an external device 104 implemented in accordance with embodiments herein. The IMD 100 and the IPS 150 are implanted within the body of a patient. The external device 104 is outside of the patient body. The external device 104 may be a reader, a programmer, an external defibrillator, a workstation, a portable computer (e.g., laptop or tablet computer), a personal digital assistant, a cell phone (e.g., smartphone), a bedside monitor, a remote care server, a wearable device (e.g., smart watch), EKG leads, and the like. The IMD 100 may represent a cardiac monitoring device, a pacemaker, a cardioverter, a cardiac rhythm management device, a defibrillator, a neurostimulator, a leadless monitoring device, a leadless pacemaker, and the like, implemented in accordance with embodiments herein. The IMD 100 may be a dual-chamber stimulation device capable of treating both fast and slow arrhythmias with stimulation therapy, including cardioversion, defibrillation, anti-tachycardia pacing and pacing stimulation, as well as capable of detecting heart failure, evaluating its severity, tracking the progression thereof, and controlling the delivery of therapy and warnings in response thereto.

[0093] In some embodiments, the system 101 can include the IMD 100 and/or the IPS 150 that acquire the periodic signals indicative of heart rate and/or pressure signals indicative of heart rate. In still other embodiments, the system 101 can include one or more wearable device 170 that is not fully implanted within the patient. The wearable device 170 may be partially or entirely external to the skin of the patient, including one or more device such as a smartwatch, EKG leads, Holter monitor, continuous glucose monitor, and smart apparel that acquire periodic signals indicative of hemodynamic function. The wearable device 170 can communicate with one or more of the IMD 100, IPS 150, the external device 104, and other remote computing device/system.

[0094] The IMD 100 includes a housing 106 that is joined to a header assembly 108 that holds receptacle connectors connected to a right ventricular lead 130 and an atrial lead 120, respectively. The atrial lead 120 includes a tip electrode 122 and a ring electrode 123. The right ventricular lead 130 includes an RV tip electrode 132, an RV ring electrode 134, an RV coil electrode 136, and an SVC coil electrode 138. The leads 120 and 130 detect CA signals or intracardiac electrogram (IEGM) signals that are processed and analyzed. The IMD 100 includes one or more processors that can process the IEGM signals and/or pressure signals acquired by the IPS 150.

[0095] The IMD 100 may be implemented as a full-function biventricular pacemaker, equipped with both atrial and ventricular sensing and pacing circuitry forfour chamber sensing and stimulation therapy (including both pacing and shock treatment). Optionally, the IMD 100 may further include a coronary sinus lead with left ventricular electrodes. The IMD 100 may provide full-function cardiac resynchronization therapy. Alternatively, the IMD 100 may be implemented with a reduced set of functions and components. For instance, the IMD may be implemented without ventricular sensing and pacing.

[0096] The IPS 150 is configured to be implanted at a location remote from the electrodes of the leads 120 and 130. The IPS 150 may be implanted in a blood vessel, such as an artery or vein. In some embodiments, the IPS 150 is implanted within the pulmonary artery (PA), while in other embodiments, the IPS 150 is implanted within the aortic artery. The IPS 150 may be anchored to the vessel wall of a blood vessel using one or more expandable loop wires. Optionally, instead of the loop wire, the IPS 150 may be attached to the end of a self-expandable stent and deployed into the blood vessel through a minimally invasive method. It should be understood that the sensor may be implanted and fixed in place utilizing other configurations. The IPS 150, when disposed within the PA or other vessel, is configured to sense pressure (e.g., blood pressure, arterial pressure), and to generate signals indicative of the pressure.

[0097] Figure 2 illustrates a block diagram of the system 101 formed in accordance with embodiments herein, showing some of the components of the IPS 150, IMD 100, external device 104, and wearable device 170. The IPS 150 comprises a sensing circuit 152, one or more controller 154, an optional a power source 156, a communications circuit 158 and a memory 160. By way of example, the IPS 150 may be implemented in accordance with one or more aspects of the sensors described in U.S. Published Application 2023/0109023, filed August 18, 2022 and titled “SYSTEM AND METHOD FOR INTRA-BODY COMMUNICATION OF SENSED PHYSIOLOGIC DATA”, the complete subject matter of which is incorporated herein by reference in its entirety. The controller 154 includes one or more processors 155. The one or more processors 155 are operably coupled to the memory 160. The IPS 150 includes a housing 151 that holds and encapsulates the sensing circuit 152, the controller 154, the power source 156, the communications circuit 158, and the memory 160, to protect these components from the harsh organic environment of the body. The housing 151 may be hermetically sealed.

[0098] In some embodiments, the IPS 150 is the CARDIOMEMS (Atlanta) heart sensor. As described by U.S. Pat. No. 9,265,428 entitled “Implantable Wireless Sensor,” and incorporated herein by reference in its entirety, these sensors are MicroElectroMechanical Systems (MEMS)-based sensors that are implanted in the pulmonary artery, more particularly in the distal pulmonary artery branch and are configured to be energized with RF energy to return high- frequency, high-fidelity dynamic pressure information from a precisely-selected location within a patient's body. In some embodiments, the IPS 150 may be a passive sensor, such as the sensor 1104 shown in Figure 11. The sensor 1104 can have anchor loops that hold it in place within a vessel. By way of example, the sensor 1104 can be a completely sealed capsule that uses the MEMS technology. As the sensor 1104 is powered by radio frequency (RF) energy, it may not require a battery or other internal power source. The sensor 1104 can include components and/or functionality for sensing pressure (e.g., sensing circuit 152), communicating (e.g., RF 157, communications circuit 158), and may include some processing capability (e.g., microcontroller 154, processor(s) 155). In other embodiments, the sensor 1104 may not include active circuits. As used herein, the term IPS 150 can also refer to the sensor 1104.

[0099] The sensing circuit 152 is configured to sense and collect pressure data (e.g., pulse pressure) and to generate pressure signal(s) indicative of the pressure data. For example, the sensing circuit 152 of an implantable pressure sensor (e.g., IPS 150) senses pressure, on-demand and/or on a schedule, over a period of time that includes a plurality of cardiac cycles, and generates a pressure signal based on the sensed pressure. The signals generated by the sensing circuit 152 represent electrical signals. Electrical parameters of the signals, such as voltage, current, capacitance, inductance or resistance, may vary based on a level of the pressure. The sensing circuit 152 includes one or more sensing elements that sense the pressure and circuitry that generates the electrical signals indicative of the pressure. In some embodiments, the one or more processor 155 collects multiple sensor output signals and converts such signals to meaningful information that the one or more processor then uses to build a pressure signal based on the pressure. The IPS 150 is a highly specialized component that is neither typical nor common, and as discussed herein, senses pressure within the body, and in some embodiments generates a pressure signal using one or more processors 155. In other embodiments, the RF module 157 generates an RF response to an external energizing signal, which is received and interpreted by an external device.

[00100] The controller 154 may be implemented as a microcontroller unit or another processor configuration. The controller 154 can perform at least some of the operations described herein to collect real-time on-demand measurements and/or scheduled measurements by generating physiologic data and can communicate the physiologic data to at least a second device, in some cases without requiring patient interaction or external energy delivery at the time of data generation and/or communication. The controller 154 represents hardware circuitry that includes and/or is connected with the one or more processors 155 (e.g., one or more microprocessors, integrated circuits, field programmable gate arrays, etc.). In some embodiments, some or all of the functions of the IPS 150 may be powered by an external device 104 positioned outside the skin of the patient in proximity to the IPS 150.

[00101] The controller 154 includes and/or is connected to the memory 160, which is a tangible and non-transitory computer-readable storage medium. The memory 160 stores program instructions (e.g., software) that are executed by the one or more processors 155 to perform the operations of the IPS 150 described herein. The memory 160 additionally may store the physiologic data (e.g., pressure signals) that is generated by the sensing circuit 152. The memory 160 may store the physiologic data until the IPS 150 transmits the physiologic data to the IMD 100 and/or the external device 104, and/or operate as a memory loop by deleting the oldest data as new data is acquired. For example, the controller 154 can prepare and send pressure data collected by the IPS 150, such as over time (e.g., 10 seconds, 18, seconds, 30 seconds, one minute, etc.) to the IMD 100.

[00102] In some embodiments, an external device 104 can communicate with the IPS 150 and may optionally power the IPS 150 or passive sensor 1104. In this example, the external device 104 may be a product such as a pillow, blanket, or device outside the body that is positioned in proximity to the IPS 150, 1104. The patient may facilitate taking regular readings of the IPS 150, 1104, such as one a day or week, and/or may conduct readings on-demand. In some cases, these readings may be 18 seconds long or longer. In other embodiments, such as during a medical procedure (e.g., catheter delivery procedure) or visit to a medical facility, the external device 104 may conduct readings on-demand that in some cases can be shorter, such as 10 seconds. The length of time the pulmonary pressure data is recorded and used for analysis may be adjusted and/or programmable, such as by a medical practitioner using an external device 104 and/or over a network.

[00103] The IPS 150 can include processing modules that are included and/or stored in the controller 154 and/or memory 160. A dicrotic notch detection module 178 can include program instructions that can be stored, for example, in memory 160. The dicrotic notch detection module 178 can analyze the pressure signals acquired by the IPS 150 and/or CA signals from the IMD 100, for a predetermined amount of time such as 10 seconds, 18 seconds, 20 seconds, etc., to identify the location of the dicrotic notch within the pressure signals as discussed further below. In some embodiments, the dicrotic notch detection module 178 utilizes a systolic peak analysis module 162 that can include program instructions that can be stored, for example, in memory 160. The systolic peak analysis module 162 can analyze the pressure signals acquired by the IPS 150 and/or CA signals from the IMD 100, for a predetermined amount of time such as 10 seconds, 18 seconds, 20 seconds, etc., to identify peaks within the pressure signals using methods and systems disclosed in the PCT application number PCT/US24/52734, filed October 24, 2024, claiming priority to application serial number 63/596,402, filed November 06, 2023, entitled “System and Method for Diastolic-Enhanced Systolic Peak Detection”, which is herein incorporated by reference in its entirety. For example, the systolic peak analysis module 162 can analyze data acquired by the IPS 150 or the IMD 100 to identify and remove effects of noise and/or arrythmia from the signal. The systolic peak analysis module 162 can segment the signal into pseudo-systolic and pseudo-diastolic segments and define valid heartbeats based on modified systolic content. It should be understood that other automated, computer implemented methods and systems can be used to identify the peaks within the pressures signals.

[00104] The IPS 150 can further include an arrhythmia analysis module 186 that includes program instructions that can be stored, for example, in memory 160. The arrhythmia analysis module 186 can receive and process data associated with the dicrotic notch and determine if the heartbeats are normal or if the heartbeats are abnormal/arrhythmic. The IPS 150 can monitor hemodynamic conditions of the patient using the normal/abnormal/arrhythmic information.

[00105] In accordance with new and unique aspects, a technical advantage of identifying the dicrotic notch is provided, which is essential for measuring systolic and diastolic durations, as well as determining cardiac function and monitoring hemodynamic functions of the patient. A further advantage is that the dicrotic notch and arrythmia, if present, can be reliably detected from a small number of beats, such as may be present in 10 seconds or 18 seconds of recorded signals.

[00106] In other embodiments, a dicrotic notch detection module 180, an arrhythmia analysis module 188, and a systolic peak analysis module 164 in the IMD 100, similar to the corresponding modules of the IPS 150, can process the pressure signals sensed and/or obtained by the IPS 150 and/or process CA signals to facilitate determining the locations of the dicrotic notch and monitoring of hemodynamic conditions of the patient as discussed herein. The IMD 100 can, in some cases, utilize a communications circuit 172 to wirelessly instruct the IPS 150 to acquire pressure signals for a predetermined length of time. In other cases, the IMD 100 can receive the pressure signals from the IPS 150 at random times, periodically on a schedule (e.g., once a day, twice a day), as a result of the IPS 150 detecting a predetermined condition and acquiring signals, and the like. The IMD 100 can also utilize the communications circuit 172 to send and receive data to/from the external device 104 and/or the wearable device 170. Although not shown, the IMD 100 also includes at least one controller, at least one processor, and memory to facilitate the operations discussed herein.

[00107] In some embodiments, a dicrotic notch detection module 182, an arrhythmia analysis module 190, and a systolic peak analysis module 166 in the external device 104, similar to the corresponding modules of the IPS 150, can similarly process the pressure signals sensed by the IPS 150 and/or CA signals sensed by the IMD 100 to facilitate determining the locations of the dicrotic notch and monitoring of hemodynamic conditions of the patient as discussed herein. The external device 104 can utilize a communications circuit 174 to wirelessly instruct the IPS 150 to acquire pressure signals and/or the IMD 100 to acquire CA signals. The external device 104 optionally may also provide power to the IPS 150, such as RF power to energize the sensing circuit 152, and may communicate bidirectionally with the wearable device 170. Although not shown, the external device 104 also includes at least one controller, at least one processor, and memory to facilitate the operations discussed herein.

[00108] In still further embodiments, a dicrotic notch detection module 184, an arrhythmia analysis module 192, and a systolic peak analysis module 168 in the wearable device 170, similar to the corresponding modules of the IPS 150, can similarly process the pressure signals sensed by the IPS 150 and/or CA signals sensed by the IMD 100 to facilitate determining the locations of the dicrotic notch and monitoring of hemodynamic conditions of the patient as discussed herein. The wearable device 170 may obtain periodic data continuously, on-demand, based on a schedule, periodically, etc. Systolic peak analysis module 168, similar to the systolic peak analysis module 162 of the IPS, can process the periodic data of predetermined lengths (e.g., 10 seconds, 18 seconds, 30 seconds, more than 30 seconds). In other cases, the wearable device 170 can utilize communications circuit 176 to transmit the obtained periodic data to the external device 104 for processing. In still further embodiments, the wearable device 170 may request and/or receive pressure signals from the IPS 150 and/or CA signals from the IMD 100. Although not shown, the wearable device 170 also includes at least one controller, at least one processor, and memory to facilitate the operations discussed herein.

[00109] Each of the devices, IMD 100, IPS 150, external device 104, and wearable device 170 can automatically process the sensed and/or obtained data in real-time or near real-time. Further, external devices such as the external device 104 and wearable device 170 can display a heart rate based on the valid heartbeats, notice of arrythmia detected, cardiac output based on the valid heartbeats, pressure such as arterial pressure, treatment notification(s), and/or treatment recommendation(s). Although not shown, the external device 104 and wearable device 170 can include one or more display capable of displaying text, graphs, accepting input (e.g., touchscreen), and the like. Further, the external device 104 and/or wearable device 170 may communicate with a network, such as to transmit patient data (e.g., raw signal data, processed signal data, results of processing) and information to a remote location such as a patient care network, for analysis, processing, and the like.

[00110] In still further embodiments, processing of the pressure signals and/or CA signals may be split among one or more of the IMD 100, external device 104, IPS 150, wearable device 170, and/or other remote processor.

[00111] In some embodiments, the controller 154 includes and/or is connected with an internal clock 153 or timer. The clock 153 may be used to cycle the IPS 150 between wake and sleep modes to conserve electrical energy. The controller 154 may refer to the clock 153 to determ ine when to activate the sensing circuit 152 to generate the signals indicative of the pressure according to a data collection schedule. For example, if the data collection schedule in the memory 160 indicates that new physiologic data should be generated at a specific time (e.g., 6 AM) of the current day, or, for example, at intervals such as every minute, two minutes, hour, etc., then the controller 154 can utilize the clock 153 to determine when it is the specific time to activate the sensing circuit 152 according to the schedule, such that the physiologic data is generated and collected in realtime at specific prescribed times.

[00112] The communications circuit 158 is operably connected to the controller 154 via conductive elements. The communications circuit 158 communicates with the IMD 100, wearable device 170, and/or the external device 104. The communications circuit 158 may be communicatively connected to the IMD 100 via an intra-body bidirectional link, which enables the IPS 150 to transmit information (e.g., data) to the IMD 100 and receive information/requests from the IMD 100. The communications circuit 158 may include an RF module 157 and/or a conductive communication module 159. The RF module 157 includes an antenna for sending and receiving RF signals. In some cases, the processor(s) 155 can direct the IPS communications circuit 158 to transmit to an IMD communications circuit 1064 and/or communication modem 1042 (both of Figure 10) a request for the CA signals, and receive the CA signals. The conductive communication module 159 includes at least two spaced-apart electrodes, connected via a conductive wire or cable, that are powered to create a polarized electric field around the IPS 150.

[00113] The optional power source 156 supplies electrical energy to power some or all of the operations of the IPS 150. The power source 156 may include one or more secondary (e.g., rechargeable) batteries, one or more primary batteries, one or more capacitors, and/or associated circuitry, such as inductive coils, charging circuits, and the like.

[00114] In other embodiments, the IPS 150 can receive power from the IMD 100, such as through a wired connection. In some cases, the wired connection can also provide at least a portion of the communications between the IPS 150 and the IMD 100. In still further embodiments, the IPS 150 can receive power wirelessly from the external device 104, such as through near field communication (NFC) via an antenna positioned outside the patient and proximate to the IPS 150. [00115] In operation, the controller 154 may directly convert, or manage conversion of, the signals from the sensing circuit 152 to digital physiologic data. The controller 154 may execute the program instructions stored in the memory 160 to activate the sensing circuit 152 to generate the signals indicative of the pressure. The controller 154 may activate the sensing circuit 152 on-demand in response to receiving a request (e.g., a data collection instruction) from another device, such as the IMD 100, or at a prescribed time according to a schedule stored in the memory 160. In some embodiments the controller 154 may activate the sensing circuit 152 on an on-going basis or near-on-going basis, acquiring and storing pressure data in the memory 160, such as in a loop, keeping the most recently acquired data. The controller 154 also executes the program instructions to convert the signals from the sensing circuit 152 to physiologic data indicative of the pressure. After converting, the controller 154 stores the physiologic data in the memory 160. In an embodiment, the controller 154 (e.g., the one or more processors 155 thereof) is configured to digitize the signals generated by the sensing circuit to form the physiologic data.

[00116] In accordance with embodiments described herein, the intra-body communication between the IPS 150 and the IMD 100 provides various benefits. For example, the pressure is measured by the IPS 150 and the communications circuit 158 can transfer pressure data to the IMD 100 and receive data from the IMD 100, including CA signals and requests. In other embodiments, the IPS 150 can determine and send information concerning the dicrotic notch and valid heartbeats to the IMD 100, and the IMD 100 can utilize the information concerning the dicrotic notch and valid heartbeats in analysis of data related to the patient or a larger population, provide a recommendation for treatment of the patient, provide a recommendation for adjusting a treatment of the patient, adjust a treatment of the patient based on the dicrotic notch, valid heartbeats and/or heart rate, and the like, thereby improving the patient outcome. For example, when the IMD 100 is a CRT/pacemaker, the treatment may be stimulation therapy. [00117] Similarly, the IPS 150 can determine and send the information concerning the dicrotic notch and/or valid heartbeats to the external device 104 and/or wearable device 170. In some cases, the external device 104 and/or wearable device 170 receives the pressure signal and determines the dicrotic notch, valid heartbeats and/or heart rate. The external device 104 and/or wearable device 170 can utilize the information concerning the dicrotic notch and/or valid heartbeats in analysis of data related to the patient or a larger population, provide a recommendation for treatment of the patient, provide a recommendation for adjusting a treatment of the patient, adjust a treatment of the patient based on the dicrotic notch, valid heartbeats and/or heart rate, and the like, thereby improving the patient outcome.

[00118] In accordance with new and unique aspects, a practical application is realized as the clinician uses the measurements/data resulting from the obtaining and processing of the pressure signal, CA signals, and/or other periodic signal to prescribe/change the patient’s therapy (e.g., prescribe new medication, change medication, change diet, recommend physical therapy, recommend to implant IMD, recommend to change programmed parameters of IMD/IPS already implanted, reprogram the implanted IMD/IPS).

MODWT-Based Dicrotic Notch Extraction

[00119] The dicrotic notch is a small dip in the arterial pressure waveform that occurs after the systolic peak and before the diastolic trough. It reflects the closure of the aortic/pulmonary valve, depending upon where the reading is taken (e.g., aorta, pulmonary artery) and the rebound of blood flow from the peripheral vessels. The dicrotic notch can provide useful information about the cardiovascular system and its hemodynamics.

[00120] In accordance with new and unique aspects, a method for computing the dicrotic notch of a blood pressure signal using maximal overlap discrete wavelet transform (MODWT) and multiresolution analysis (MRA) is disclosed herein. The MODWT is a variant of the discrete wavelet transform (DWT) that preserves the time resolution of the original signal and allows for shift-invariant analysis. In other embodiments, Fast Fourier Transform (FFT) can be used, followed by deconstructing the waveform into time domain bins of frequency components.

[00121] Figures 3A and 3B (collectively referred to as Figure 3) illustrate a computer-implemented method for implementing a dicrotic notch detection algorithm for detecting the location of the dicrotic notch in cardiac pressure signals in accordance with embodiments herein. The method describes the steps for processing and analyzing the signal of arterial pressure (AP) such as pulmonary arterial pressure (PAP) and its derivative (^) to identify the dicrotic notch, which is a feature of the AP waveform that indicates the closure of the aortic/pulmonary valve. The method can equally be applied to identify other features of the AP waveforms, such as Pi inflection point, closure of the tricuspid valve or mitral valve, etc.

[00122] The operations of Figure 3 may be implemented by hardware, firmware, circuitry and/or one or more processors housed partially and/or entirely within an IMD 100, an IPS 150, an external device 104, wearable device 170, a local external device, remote server or more generally within a health care system. Optionally, the operations of Figure 3 may be shared across devices, and thus partially implemented by one or more of an IMD 100, an IPS 150, an external device 104, a wearable device 170, a local external device, remote server or more generally within a health care system. For example, the IMD 100 includes IMD memory and one or more IMD processors, the IPS 150 includes IPS memory and one or more IPS processors, the external device 104 includes external device memory and one or more external device processors, and the wearable device 170 includes wearable memory and one or more wearable processors, and further, other of the external devices/systems (e.g., local, remote or anywhere within the health care system) that may implement the operations of Figure 3 include external device memory and one or more external device processors. [00123] Although Figure 3 primarily discusses the detection of the dicrotic notch in pulmonary arterial pressure signals acquired by the IPS 150, it should be understood that the methods and systems apply equally to pressure signals acquired by a pressure sensor located in other locations within the body such as aortic signals (e.g., aorta), venous signals, etc., and CA signals acquired by the IMD 100.

[00124] Further, although Figure 3 discusses the detection of the dicrotic notch, the algorithm can be applied to other feature(s) of a pressure waveform that has a change in slope. If a different feature is to be identified within the pressure waveform, the timing and/or frequency component bins may be different. For example, on the rise side of the pressure waveform, the Pi inflection point may be determined. For example, the inflection point in the pressure waveform is significant because it represents the point where the forward pressure wave, generated by the heart's contraction, meets the reflected wave coming back from the peripheral circulation. This interaction affects the shape of the pressure waveform and has implications for cardiovascular health. The inflection point can indicate the timing and magnitude of wave reflections in the aorta, which are important determinants of central pressure. These reflections can influence the workload on the heart and the efficiency of blood flow. For instance, if the reflected wave returns during systole, it can lead to increased systolic pressure and afterload on the heart, potentially contributing to conditions like systolic hypertension. In addition, the position of the inflection point can provide insights into arterial stiffness and the condition of the vascular system. An earlier occurrence of the inflection point can suggest stiffer arteries and an earlier return of the reflected wave, which may be associated with various cardiovascular risks. [00125] At 302, one or more processors, such as of the IPS 150, sense (e.g., collect) pressure data, such as for 10 seconds, 18 seconds, etc., and generate a pressure signal (e.g., arterial pressure (AP) signal) that is based on the pressure data. In some cases, the external device 104 can energize the sensing circuit 152 of the IPS 150 and generate the pressure signal based on returned signals from the IPS 150. In other embodiments, one or more processors, such as of the IMD 100, sense (e.g., collect) cardiac activity (CA) and generate CA signals based on the CA.

[00126] At 304, one or more processors store the pressure signals in a memory in the IPS 150, such as memory 160 of Figure 2, and/or memory in the external device 104, etc. Alternatively or additionally, one or more processors store the CA signals, such as in a memory of the IMD 100. In some embodiments, the memory can store the signals for a predetermined amount of time, such as 10 seconds, 30 seconds, one minute, two minutes, or more, depending upon the space available. The associated device can discard older signals in favor of storing more recently acquired signals. In some embodiments the signals can be stored in a running loop, such that older data is overwritten or otherwise deleted as more signals are collected and stored. In some embodiments, the IPS 150 can collect pressure signals and/or the IMD 100 can collect CA signals at predefined intervals, substantially in real-time by continuously sensing pressure/cardiac activity, and/or on-demand, such as upon receiving a signal or other request to sense pressure/cardiac activity, such as for a predetermined amount of time.

[00127] Optionally, at 306 the one or more processors transmit the pressure signal to another device. For example, the IPS 150 can transmit the pressure signals to the IMD 100, the external device 104, and/or wearable device 170. In other cases, the IMD 100 can transfer the CA signals to the external device 104, wearable device 170, and/or IPS 150.

[00128] Figure 4A shows a graph 400 of a pulmonary pressure waveform 402 based on an arterial pressure signal (e.g., AP signal) that has been acquired by the IPS 150 over time in accordance with embodiments herein. The pressure waveform 402 is periodic, having a series of peaks and valleys. Vertical axis 404 indicates a measure of pulse pressure in millimeters of mercury (mmHg), and horizonal axis 406 indicates time in seconds (s). In this example, the pressure waveform 402 has been acquired for approximately 10 seconds and is variable and/or periodic between approximately 20 mmHg to approximately 50 mmHg. In some embodiments, vertical axis 404 indicates a measure of a magnitude of a pulse portion of the pulse pressure associated with the IPS 150, and in some embodiments the magnitude can be an amplitude of the pulse portion of the pulse pressure. By way of example, for the IPS 150 that is positioned in the pulmonary artery, the IPS 150 measures the pulsatility that is created by the regular contraction of the left ventricle. In ventricular tachycardia (VT) and some cases of defibrillation, there is no organized contraction, so pulse pressure and thus pulsatility will diminish.

[00129] Returning to Figure 3, the AP signal can be preprocessed and filtered, which may be accomplished in a single device, be split across more than one device, and/or processed wholly or partially by the device that acquired the signal data. For simplicity, the method will be discussed from the perspective of being processed by the external device 104.

[00130] At 308, the one or more processors remove a linear trend from the signal. In some examples, a linear model (e.g., straight line) is fit to the signal and then subtracted from the original signal, so that the resulting signal has zero mean and no linear trend. This operation is performed to eliminate any bias or drift in the signal that may affect the analysis.

[00131] As used herein, the term “linear trend” shall mean a statistical term that describes the tendency of a variable to change over time in a consistent and predictable way. A linear trend can be represented by a straight line. One way to estimate the linear trend of a data set is to use linear regression, which finds the best-fitting line that minimizes the sum of squared errors between the observed data points and the line. The equation of the line is usually written as y = ax + b, where y is the variable value, x is the time point, a is the slope, and b is the intercept. The slope and the intercept are called the regression coefficients, and they can be calculated using some mathematical formulas. [00132] Turning to Figure 4B, this figure shows a graph 420 of linear detrended waveform 422, based on the pressure waveform 402 of Figure 4A, in accordance with embodiments herein. Again, vertical axis 424 indicates a measure of pulse pressure and horizonal axis 426 indicates time. In this example, the linear detrended waveform 422 crosses zero pressure and is variable and/or periodic, extending above and below zero, between less than -10 mmHg and nearly 20 mmHg.

[00133] Returning to Figure 3, at 310 the one or more processors generate detrended AP signal by removing nonlinear trends from the linear detrended waveform 422 to enhance the signal quality and reduce any artifacts or distortions that may affect the analysis. Accordingly, the signal average is now at zero. For example, baseline wandering and/or high frequency noise can be removed from the linear detrended waveform 422, such as by applying a band pass filter, an independent component analysis, a polynomial fitting, or a Maximal overlap discrete wavelet transform (MODWT) to the signal. Low frequency content can also be subtracted. For example, the pulmonary pressure signal may include a low frequency wave that can be a respiratory artifact. It should be understood that other methods may be used.

[00134] As used herein, the term “nonlinear trend” shall mean a pattern of variation in a data set that does not follow a straight line or a simple curve. It means that the relationship between the dependent variable and the independent variable is not linear, and the rate of change is not constant. Nonlinear trends can be influenced by many factors, such as respiratory variations, activity of the patient, and the presence of arrhythmias.

[00135] At 312, the one or more processors calculate the first derivative of the detrended AP signal (^) by using a numerical method, such as finite difference or central difference. The first derivative can be referred to herein as the AP derivative signal. This operation is performed to obtain the rate of change of the pressure signal, which is useful for identifying the features of the pulse waveform, such as systolic peaks, diastolic peaks, and dicrotic notch.

[00136] At 314-320, the one or more processors apply maximal overlap discrete wavelet transforms (MODWT) and multiresolution analysis (MRA). MODWT computes the wavelet transform and MRA refers to breaking up a signal into components. These operations decompose the detrended AP signal and its first derivative, AP derivative signal, into different frequency components by using a wavelet transform, which is a mathematical tool that allows for time-frequency analysis of signals. For example, wavelet transform can be applied to the pressure signal to decompose the detrended AP signal and the AP derivative signal into frequency component (FC) bins. The MODWT preserves the time information of the original signal and does not reduce its length, unlike other wavelet transforms. The MRA reconstructs the signal at different scales by summing up the wavelet and scaling coefficients at each level. These operations are performed to isolate and extract the relevant frequency components of the signal that correspond to the dicrotic notch or other desired mechanical event.

[00137] Referring to Figure 8, this figure illustrates a multiresolution analysis based on wavelet analysis in accordance with embodiments herein. Graph 800 illustrates a pressure waveform such as the AP signal 802 acquired overtime (e.g., approximately 10 seconds). Vertical axis 804 indicates a measure of pulse pressure and horizonal axis 806 indicates time. The AP signal 802 is a time domain signal, and the wavelet transformation is a time domain based transformation.

[00138] The AP signal 802 is decomposed into FC bins one through 12 as shown in graph 808, and thus each of the FC bins includes a portion of the AP signal 802 for a select frequency range over a select time frame. Vertical axis 810 indicates amplitude, horizontal axis 812 indicates frequency levels (e.g., FC bins), and depth axis 814 indicates time in seconds. In this example, there are 12 FC bins. Each bin has a corresponding frequency band or range of frequency, and the bands are consecutive/contiguous, increasing in frequency as the FC bin number decreases.

[00139] Figure 9 illustrates exemplary MODWT levels that each correspond to a different frequency band in accordance with embodiments herein. Each of the frequency levels can be referred to as an FC bin. Chart 900 shows levels 1-11 in column 1 902, frequency range in hertz in column 2904, and physiological sources of the frequencies in column 3 906. The FC bins separate frequency components that correspond to different aspects of the cardiac cycle and different interactions between the heart and pulmonary circulation and/or aortic circulation. For example, FC bins (e.g., levels one-four) generally correspond to heart rate harmonics, FC bins (e.g., levels five-seven) generally correspond to heart rate activity such as heart valve activity, FC bins (e.g., levels eight and nine) generally correspond to respiratory activity, and FC bin (e.g., level 10) generally corresponds to Mayer waves, related to vasomotor activity. In some embodiments, some aspects of the cardiac cycle can correspond to heart valve activity (e.g., opening, closing) and some interactions between the heart and pulmonary circulation can correspond to respiratory activity (e.g., inspiration, expiration). In other embodiments, some aspects of the cardiac cycle can correspond to mechanical events, It should be understood that the specific frequencies shown associated with each level are examples only and are not limiting as other frequency range(s) may be defined.

[00140] According to new and unique aspects herein, it is advantageous to deconstruct the signal into different bins because different mechanical events occur with different frequency response. In other words, the AP signal is filtered into the frequency domain that is relevant to the desired event. For example, frequency components associated with the dicrotic notch can be found in one or more particular bin. In general, frequency components associated with movement of the aortic valve and pulmonary valve would be found in the same bin(s) as the dicrotic notch, frequency components associated with the Pi inflection point may be found in the same or one or more other bins, while frequency components associated with movement of the tricuspid and mitral valves may also be found in the same or one or more other bins. Deconstructing facilitates the isolation of particular mechanical events or events in the waveform, and the events of interest, such as the pulmonary valve and/or aortic valve, can then be focused on without having to evaluate other events, such as by excluding frequency components associated with movement of other events such as the tricuspid and mitral valves. [00141] Returning to Figure 3, at 314, the one or more processors apply the MODWT to

(e.g., AP derivative signal) and to the detrended AP signal using

the Daubechies 3 (db3) wavelet (or any other wavelet) and the maximum possible decomposition level. In some embodiments, the number of maximum decomposition levels is 11. In other words, the one or more processors apply a wavelet transformation to the AP signal and the AP derivative signal to decompose the signals into FC bins and derivative FC bins, respectively. It should be understood that other wavelet transformations and decomposition levels may be used to decompose the signal.

[00142] At 316, the one or more processors perform a multiresolution analysis (MRA)

(e.g., AP derivative signal) and on the detrended AP signal

using the db3 wavelet or any other wavelet.

[00143] At 318, the one or more processors, for the detrended AP signal, reconstruct a signal using a subset of the signal levels or FC bins, forming a filtered version, Pmtered, of the detrended AP signal (e.g., filtered AP signal). For example, in some embodiments, the one or more processors use levels 3 to 5 of the MODWT (e.g., frequency range 7.79 Hz -392 Hz as shown in Figure 9) to reconstruct the signal based on features of interest (e.g., do not reconstruct the signal during times when the dicrotic notch would not be found).

[00144] At 320, the one or more processors, for the AP derivative signal, reconstruct a signal using a second subset of the signal levels or derivative FC bins, forming a filtered version of the AP derivative signal (e.g., derivative dicrotic notch signal). The derivative FC bins used in 320 and FC bins used in 318 may be the same or different. For example, in some embodiments, the one or more processors use levels 2 to 4 of the MODWT (e.g., frequency range 7.84 Hz -15.6 Hz) to reconstruct the signal, herein called derivative dicrotic notch signal and also referred to in the equations as P_DiCrotic notch MRA ■

[00145] In both 318 and 320, the range of levels or bins selected should be great enough to cover heart rate variability, and also eliminate very high frequency and low frequency, both of which are not related to the frequencies of the pulmonary and aortic valves. The bins shall be selected to encompass the dicrotic notch or other feature of interest. It should be noted that other levels or bins may be used to cover the frequency ranges of other features within the waveforms, such as those associated with the Pi inflection point, or movement of the tricuspid valve and/or mitral valve.

[00146] At 322 and 324, the process identifies start and end times of a rough estimate of a time range for where the method will search the cardiac cycles for the dicrotic notch. Binary filters determined at 322 and 324 are based on the assumption that the dicrotic notch occurs after a systolic peak and before a diastolic peak.

[00147] At 322, the one or more processors apply a threshold function to the reconstructed filtered AP signal Pfu_tered (signal generated at 318) to generate Ppseudo-systoiic- The threshold function assigns a value of 1 to any positive element of the filtered AP signal, and a value of 0 to any negative or zero element to generate the binary filter. The operation then creates a binary filter from the pseudo-systolic pressure Ppseudo-systoiic. This operation is performed to indicate where the systolic peaks are located in the filtered AP signal, which is useful for determining the timing of the start of the dicrotic notch. The binary filter is used further below to isolate negative parts of the first derivative signal.

[00150] At 324, the one or more processors apply a logical function to the original pressure signal (e.g., AP signal of 302) to create another filter inotch positive- The logical function assigns a value of 1 to any element of the AP signal that is between the peak systole and the diastole, and a value of 0 to any other element. For example, a peak detection algorithm as discussed herein can be used to identify locations of peak systole and diastole before applying the logical function. The filter is used further below to isolate the negative parts of the first derivative signal.

[00152] Figures 4C and 4D show visual examples of the binary filters of 322 and 324 extracting a dicrotic notch search area from the filtered AP signal in accordance with embodiments herein. Figure 4C shows a graph 430 of the binary filter 434, t_{notch negative}, of 320 in accordance with embodiments herein. Vertical axis 436 indicates a measure of amplitude and horizonal axis 438 indicates time (s). The binary filter 434 is used below to isolate the negative parts of the derivative dicrotic notch signal (e.g, ^Dicr otic notch MRA )■

[00153] Figure 4D shows a graph 440 of the binary filter 442 in accordance with embodiments herein. Vertical axis 444 indicates a measure of amplitude and horizonal axis 446 indicates time. The binary filter 442 is used below to isolate the positive parts of the derivative dicrotic notch signal (e.g., P_Dicrotic notch MRA )■

[00154] As discussed below, by separating the MRA-reconstructed derivative signal (e.g., derivative dicrotic notch signal determined at 320) into positive and negative segments and multiplying the segments by the binary filters, better defined time intervals (e.g., dicrotic notch search areas) that contain the dicrotic notch are determined. In other words, the portion of the waveform within each cardiac cycle that includes the dicrotic notch is identified. [00155] At 326, the one or more processors divide the derivative dicrotic notch signal determined at 320 into positive and negative segments.

[00158] At 328, the one or more processors multiply the segments by the binary filters determined at 322 and 324.

[00161] At 330, the one or more processors square the signals determined in 328, resulting in a signal that is positive and magnified to facilitate the detection of its peaks.

[00164] Figures 4E-4H illustrate the process of generating the negative and positive parts of the derivative dicrotic notch signal in accordance with embodiments herein. The negative and positive parts of the derivative dicrotic notch signal are then squared to make them positive and amplified (as shown in Figure 4H). The peaks of these signals are used to indicate the approximate location within which the dicrotic notch is located.

[00165] Figure 4E is an example of an original AP signal waveform 456, similar to Figure 4A, that is based on a pressure signal that has been acquired by the IPS 150 over time in accordance with embodiments herein. Graph 450 has vertical axis 452 indicating a measure of pulse pressure and horizonal axis 454 indicates time. [00166] Figure 4F is an example of a detrended AP derivative waveform 464 based on the AP signal waveform 456 of Figure 4E in accordance with embodiments herein. Graph 458 has vertical axis 460 indicating a change of pressure and horizonal axis 462 indicates time. For example, the detrended AP derivative waveform 464 can be calculated at 312 of Figure 3.

[00167] Figure 4G illustrates a derivative dicrotic notch signal 466, such as the signal reconstructed in 320, based on the detrended AP derivative 464 in accordance with embodiments herein. Graph 468 has vertical axis 470 indicating a change of pressure and horizonal axis 472 indicates time. The reconstructed derivative dicrotic notch signal 466 is a cleaner signal as it only includes the desired frequency components from the desired bins. For example, the derivative dicrotic notch signal 466 is the reconstructed signal from the detrended AP derivative using the MODWT.

[00168] Figure 4H illustrates a squared reconstructed signal 474 based on the derivative dicrotic notch signal 466 in accordance with embodiments herein. Graph 476 has vertical axis 478 indicating a change of pressure and horizonal axis 479 indicates time. The derivative dicrotic notch signal 466 has been processed as discussed in 326-330, such that the signal has been divided into positive and negative segments, the positive and negative segments have been multiplied by the binary filters, and the resultant signals have been squared. The squared reconstructed signal 474 includes negative segments 480a, 480b, 480c and positive segments 482a, 482, 482c (not all segments are individually indicated) of the derivative dicrotic notch signal 466.

[00169] Returning to Figure 3, at 332 the one or more processors identify a peak of each of the dicrotic notch negative segments 480 and dicrotic notch positive segments 482. Any peak detection method can be used, such as for example, the peak detection method disclosed in the PCT application number PCT/US24/52734, filed October 24, 2024, claiming priority to application serial number 63/596,402, filed November 06, 2023, entitled “System and Method for Diastolic-Enhanced Systolic Peak Detection”, the complete subject matter of which is herein incorporated by reference in its entirety. It should be understood that other automated, computer implemented methods and systems can be used to identify the peaks within the pressure signals. The peaks can be used to define start and end events of a dicrotic notch search area within which the dicrotic notch is located.

[00170] Figures 4I^IL illustrate the process of detecting the start and end points of a dicrotic notch search area and isolating the location of the dicrotic notch. These figures demonstrate how the start and end points of the dicrotic notch search area are detected and how the dicrotic notch search area is isolated from the derivative dicrotic notch signal 466 of the arterial pulse wave. The start and end points of the dicrotic notch search area are determined by finding the peaks of the negative and positive parts of the derivative dicrotic notch signal, respectively. The dicrotic notch search area is then obtained by multiplying the derivative dicrotic notch signal by a binary filter that is one only for the interval between the start and end points of the dicrotic notch search area. The peaks of these dicrotic notch search areas indicate the location of the dicrotic notch for each cardiac cycle, which is a feature of the arterial pulse wave that reflects the closure of the pulmonary valve.

[00171] Figure 4I illustrates peaks of the negative segments 480 and positive segments 482 in accordance with embodiments herein. Graph 488 has vertical axis 490 indicating a change of pressure and horizonal axis 492 indicates time. The graph 488 shows the squared reconstructed signal 474 of Figure 4H. Peaks 484a, 484b, 484c (not all of the peaks 484 are individually indicated) of the dicrotic notch negative segments 480 are indicated and peaks 486a, 486b, 486c (not all of the peaks 486 are individually indicated) of the dicrotic notch positive segments 482 are indicated. The peaks 484 and 486 are also referred to herein as dicrotic notch events. In Figure 4I, the peaks 484, 486 are identified based on the MRA- reconstructed dicrotic notch signal (e.g., derivative dicrotic notch signal 466) from the detrended PAP derivative signal using the MODWT. This reconstruction is advantageous because it provides the isolation of the timing of the dicrotic notch. [00172] Figure 4J illustrates the timing of the start and end events of a dicrotic notch search area 485 based on the peaks 484, 486 identified in Figure 4I in accordance with embodiments herein. For example, the peaks 484, 486 indicate start and end events, respectively, of the dicrotic notch search area 485. Graph 494 has vertical axis 496 indicating a change of pressure and horizonal axis 498 indicates time. Figure 4J identifies the approximate location along the detrended AP derivative waveform 464 for the algorithm to look for the dicrotic notch, as indicated by dicrotic notch search area 485a, 485b, 485c. For example, dicrotic notch search area 485a is located between the peak 484a (e.g., start event) and the peak 486a (end event). Similarly, the dicrotic notch search areas 485b, 485c, are located between the peaks 484b, 484c (e.g., start events) and the peaks 486b, 486c (e.g., end events), respectively.

[00173] Returning to Figure 3, at 334, using the peaks 484 and 486 of the negative and positive segments 480, 482, the one or more processors generate a final binary filter. The result of the final binary filter is “one” only for the interval between the start and end points of the dicrotic notch search area 485.

[00174] inotch- Vi Peaks of dicrotic notch negative part < P (i) < Peaks of dicrotic notch positive part ) => (P (i) = 1)

[00175] At 336, the one or more processors multiply the P_DiCrotic notch MRA (e.g., derivative dicrotic notch signal 466) determined at 320 with the final binary filter determined at 334, t_notch, to obtain final filtered signal, P_DN. For example, the final filtered signal, P_DN, corresponds with the search area 485 shown in Figure 4J. [00176] At 338, the one or more processors identify peaks within the final filtered signal P_DN. Any peak detection algorithm can be used as discussed herein. [00177] Turning to Figure 4K, this figure illustrates the result of filtering the derivative dicrotic notch signal 466 by the final binary filter t_notch in accordance with embodiments herein. Specifically, Figure 4K identifies locations of dicrotic notch 508a, 508b, 508c (not all are indicated separately) at the peak between the start and end points (e.g., peak 484 and peak 486) of the search area (e.g., dicrotic notch search area 485). Graph 500 has vertical axis 502 indicating amplitude and horizonal axis 504 indicating time. Non-zero values of final filtered signal 506 correspond to the portions of the P_Dicrottc notch MRA determined at 320 that are between the start events (e.g., peak 484) and end events (e.g., peak 486) of the dicrotic notch search area 485 as indicated on Figure 4J. Zero values of the final filtered signal 506 correspond to portions of the P_{D icr}otic notch MRA that are filtered out, as the dicrotic notch is not expected to be located there.

[00178] At 340, the one or more processors determine the locations of the dicrotic notch 508 on the AP signal, such as based on time.

[00179] Figure 4L shows the AP signal waveform 456, previously shown in Figure 4E, and illustrates the locations of peak systole, peak diastole, and the dicrotic notch in accordance with embodiments herein. Graph 510 has vertical axis 512 indicating pressure and horizonal axis 514 indicating time. Peak systole 516a, 516b, 516c (not all are individually indicated) and peak diastole 518a, 518b, 518c (not all are individually indicated) are determined using other methods known in the art, such as peak detection as discussed herein. The dicrotic notch 508a is indicated between peak systole 516a and peak diastole 518a, marking the beginning of diastole. Similarly, the dicrotic notch 508b is indicated between systole 516b and diastole 518b, and dicrotic notch 508c is indicated between systole 516c and diastole 518c, marking the beginning of diastole for those cardiac cycles.

[00180] At 342, the one or more processors monitor one or more hemodynamic conditions based on the dicrotic notch. For example, monitoring of the dicrotic notch can be accomplished over an extended period of time (days, weeks, months, years) and used within different waveform analysis to monitor chronic progression and/or trends of disease over time (e.g., trended features can be tracked over time). For example, cardiac output, heart failure, vasculature compliance, valve disease, pressure related to medication, a patient’s resistance to medication and/or need to add medication and/or adjust medication level(s), propose a treatment modification, automatically modify a setting of an IMD, propose a change in monitoring frequency, dicrotic notch changes as related to electrolytes, blood glucose, heart rate, etc. In some cases, changes in the dicrotic notch’s position in time may indicate valve disease.

Abnormal/Arrhythmic Heartbeat Detection

[00181] The dicrotic notch location, which are the points on the AP signal that signify the closure of the aortic/pulmonary heart valves, can provide vital information regarding the heartbeat. In the normal heartbeats, the closure of the valves is fairly consistent, and the valves produce a similar closure pressure that can be perceived as a “sound” or “clamping” of the valve. The MODWT reconstructed signal (e.g., signals reconstructed at 318 and 320 of Figure 3) can be viewed as a signal that contains some information about this “clamping” sound for each beat. Normal heartbeats have similar closure patterns, as can be seen in Figures 6A and 6B, discussed further below. However, for abnormal/arrhythmic heartbeats, the extra or abnormal beats disrupt the normal valve closure and alter the amplitude of the valve “clamping”, as shown in Figures 7A and 7B, also discussed further below.

[00182] Figure 5 illustrates a method for determining abnormal/arrhythmic heartbeats based on the dicrotic notch location in accordance with embodiments herein. The operations of Figure 5 may be implemented by hardware, firmware, circuitry and/or one or more processors housed partially and/or entirely within an IMD 100, an IPS 150, an external device 104, wearable device 170, a local external device, remote server or more generally within a health care system. Optionally, the operations of Figure 5 may be shared across devices, and thus partially implemented by one or more of an IMD 100, an IPS 150, an external device 104, a wearable device 170, a local external device, remote server or more generally within a health care system. For example, the IMD 100 includes IMD memory and one or more IMD processors, the IPS 150 includes IPS memory and one or more IPS processors, the external device 104 includes external device memory and one or more external device processors, and the wearable device 170 includes wearable memory and one or more wearable processor, and further, other of the external devices/systems (e.g., local, remote or anywhere within the health care system) that may implement the operations of Figure 5 include external device memory and one or more external device processors.

[00183] The method of Figure 5 will be discussed together with Figures 6A- 7B. In general, Figures 6A and 6B indicate a relatively regular heartbeat while Figures 7A and 7B indicate an irregular heartbeat.

[00184] Figure 6A illustrates an original AP signal waveform with peak systole, peak diastole, and the dicrotic notch as determined in Figure 3, indicated in accordance with embodiments herein. Graph 600 has vertical axis 602 indicating pressure and horizonal axis 604 indicating time. Peak systole 608a, 608b, 608c (not all are individually indicated) and peak diastole 610a, 610b, 610c (not all are individually indicated) are indicated on AP signal waveform 606. Dicrotic notch 612a, 612b, 612c (not all are individually indicated) are indicated between associated peak systole 608 and peak diastole 610, marking the beginning of diastole for those cardiac cycles.

[00185] Figure 6B is similar to Figure 4K, and illustrates a waveform of a corresponding final filtered signal, which isolates the dicrotic notch feature from the arterial pulse wave in accordance with embodiments herein. Graph 620 has vertical axis 622 indicating amplitude and horizonal axis 624 indicating time in seconds (s). The dicrotic notch 612a, 612b, 612c (not all are individually indicated) is indicated on final filtered signal 626. The dicrotic notches 612 mark the peaks of the final filtered signal 626 (e.g., determined at 338) and correspond to the location and amplitude of the associated dicrotic notch.

[00186] Figure 7A illustrates another original AP signal waveform having peak systole, peak diastole, and the dicrotic notch as determined in Figure 3 indicated in accordance with embodiments herein. Graph 700 has vertical axis 702 indicating pressure and horizonal axis 704 indicating time. AP signal waveform 706 is shown with peak systole 708a, 708b, 708c (not all are individually indicated), peak diastole 710a, 710b, 710c (not all are individually indicated), and dicrotic notch 712a, 712b, 712c (not all are individually indicated) indicated therebetween, marking the beginning of diastole for those cardiac cycles. Figures 6A and 7A illustrate a measure of the blood pressure changes in the arteries during each cardiac cycle.

[00187] Figure 7B, also similar to Figure 4K, illustrates a waveform of a corresponding final filtered signal, which isolates the dicrotic notch feature from the arterial pulse wave in accordance with embodiments herein. Graph 720 has vertical axis 722 indicating amplitude and horizonal axis 724 indicating time in seconds (s). The dicrotic notch 712a, 712b, 712c (not all are individually indicated) is indicated on final filtered signal 726. The dicrotic notches 712 mark the peaks of the final filtered signal 726 and correspond to the location and amplitude of the dicrotic notch.

[00188] Referring to Figure 5, at 552, the one or more processors calculate the Root Mean Square of Successive Differences (RMSSD) of the final filtered signal 626, 726 at the peaks (e.g., dicrotic notch 612, 712). Let P_DN be the final filtered signal 626, 726, which is a signal that isolates the dicrotic notch feature from the arterial pulse wave. Let n be the number of dicrotic notch peaks in the signal. Let P_DN(i) be the value of the final filtered signal at the i - th dicrotic notch peak. For example, referring to Figure 6B, the number of peaks in the signal is known, as is the value of each of the peaks.

[00190] At 554, the one or more processors normalize the RMDDS by the average value of the final filtered signal 626, 726 at the peaks (e.g., dicrotic notch 612, 712). The resulting metric, Normalized Dicrotic Notch Variability (NDNV), can be used to monitor the heartbeats. The NDNV metric measures the variability of the location and amplitude of the dicrotic notch 612, 712, which is affected by the normality or abnormality of the heartbeat. Normalizing the RMSSD by the mean value of the final filtered signal at the peaks can help compare the results across different datasets or models with different scales. This metric can help diagnose and monitor various cardiac conditions and disorders that may cause abnormal/arrhythmic heartbeats. The metric can be written as:

[00192] As can be seen in Figure 6B, the peaks of the final filtered signal 626, which correspond to the location and amplitude of the dicrotic notch 612, are fairly consistent and have a similar value. Therefore, for the AP signal waveform 606, the NDNV has a small value of 0.197. This indicates that the heartbeat is regular.

[00193] Turning to Figure 7B, the peaks of the final filtered signal 726 are not consistent and have diverse values. For example, the dicrotic notches 612 indicated in Figure 6B are more regularly spaced than the dicrotic notches 712 indicated in Figure 7B. Further, the amplitudes of the dicrotic notches 612 in Figure 6B are more consistent with respect to each other compared to the amplitudes of the dicrotic notches 712 in Figure 7B. Therefore, for the AP signal waveform 706, the NDNV has a large value of 1 .183, indicating that the heartbeat has irregularity. This could be a sign of various cardiac conditions and disorders that may be abnormal/arrhythmic heartbeats, such as atrial fibrillation, ventricular tachycardia, myocardial infarction, etc.

[00194] At 556, the one or more processors compare the NDNV to a threshold. In some embodiments, the threshold can be dynamic, based on the patient’s AP reading history, fixed, or manually set by clinicians, e.g., high sensitivity or low sensitivity detection. If the NDNV is below or not greater than the threshold, flow passes to 558 and the one or more processors determine that the heartbeats are normal. At 560, optionally, the one or more processors report that the heartbeats are normal, such as by saving the information to a file, sending a message (e.g., email, text, automated phone call) to doctors, clinical team, and/or patient, saving the information in a patient Application, and the like.

[00195] Returning to 556, if the NDNV is above or greater than the threshold, flow passes to 562, and the one or more processors determine that the heartbeats are abnormal/arrhythmic. At 564, the one or more processors may report the abnormal beats, such as by sending a message, warning or other alert to doctors, clinical team, and/or the patient, and the information can be saved to memory in a file, a patient Application, etc. Technical advantages of determining the RMSSD for dicrotic notch peaks include accurately determining whether heartbeats are normal or abnormal using a signal that has been acquired in a relatively short length of time.

[00196] Flow passes from 560 and 564 to 566, and the one or more processors may monitor a hemodynamic condition of the patient based on the normal/abnormal beats as well as the dicrotic notch events. In some embodiments, monitoring can be accomplished over an extended period of time (days, weeks, months, years) to monitor chronic progression and/or trends of disease over time (e.g., trended features can be tracked over time). For example, cardiac output, heart failure, vasculature compliance, valve disease, pressure related to medication, a patient’s resistance to medication and/or need to add medication and/or adjust medication level(s), propose a treatment modification, automatically modify a setting of an IMD, propose a change in monitoring frequency, etc. In some embodiments, the IMD 100 can implement a treatment such as shocking the patient, adjusting pacing, and the like based on the dicrotic notch events.

[00197] Figure 10 shows an example block diagram of the IMD 100 formed in accordance with embodiments herein. The IMD 100 may treat both fast and slow arrhythmias, including VA (e.g., further including VF/VT, etc.), with stimulation therapy, including cardioversion, pacing stimulation, suspend tachycardia detection, tachyarrhythmia therapy, and/or the like. In some embodiments, the IMD 100 can be one of an implantable cardioverter defibrillator, pacemaker, cardiac rhythm management device, defibrillator, or leadless pacemaker but is not so limited. In some embodiments, the treatments may be initiated and/or modified based on the detected dicrotic notch events.

[00198] The IMD 100 has a housing 1040 to hold the electronic/computing components. The housing 1040 (which is often referred to as the "can," "case," "encasing," or "case electrode") may be programmably selected to act as an electrode for certain sensing modes. Housing 1040 further includes a connector (not shown) with at least one terminal 1000 and optionally additional terminals 1002, 1004, 1006, 1008, 1010. The terminals 1000, 1002, 1004, 1006, 1008, 1010 may be coupled to sensing electrodes that are provided upon or immediately adjacent the housing 1040. Optionally, more or less than six terminals 1000, 1002, 1004, 1006, 1008, 1010 may be provided in order to support more or less than six sensing electrodes. Additionally or alternatively, the terminals 1000, 1002, 1004, 1006, 1008, 1010 may be connected to one or more leads having one or more electrodes provided thereon, where the electrodes are located in various locations about the heart. The type and location of each electrode may vary. The lead can be positioned in one of a transvenous, subcutaneous, or subxiphoid position. In some embodiments, the IMD 100 can be a subcutaneous IMD coupled to an extravascular lead having a first electrode disposed along a distal segment of the lead and a second electrode disposed along a proximal segment of the lead.

[00199] The IMD 100 includes a programmable microcontroller 1020 that controls various operations of the system 101 , including cardiac monitoring. Microcontroller 1020 includes a microprocessor (or equivalent control circuitry, one or more processors, etc.), RAM and/or ROM memory, logic and timing circuitry 1032, state machine circuitry, and I/O circuitry. The timing circuitry 1032 can control the timing of the stimulation pulses (e.g., pacing rate, atrio-ventricular (AV) delay, atrial interconduction (A-A) delay, or ventricular interconduction (V-V) delay, etc.).

[00200] The microcontroller 1020 includes a dicrotic notch detection module 1036, an arrhythmia analysis module 1034, and a systolic peak analysis module 1035 in the IMD 100, similar to and including some or all of the functionality of the corresponding modules of the IPS 150, external device 104, and wearable device 170 (Figure 2) that can process the pressure signals sensed and/or obtained by the IPS 150 and/or process CA signals to facilitate monitoring of hemodynamic conditions of the patient as discussed herein.

[00201] For example, for AP signals sensed using an AP sensor, the dicrotic notch detection module can apply a wavelet transformation to the AP signal to decompose the AP signal into frequency component (FC) bins, select a subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest, reconstruct or generate, such as automatically, the subset of FC bins to form a filtered AP signal, detect dicrotic notch events along the filtered AP signal, and the IMD, IPS, external device and/or wearable device can monitor a hemodynamic condition of the patient based on the dicrotic notch events. In some cases, the subset of the FC bins selected includes frequency components associated with movement of at least one of the aortic or pulmonary valves. In some cases, each of the FC bins includes an AP signal for a select FC over a select time frame.

[00202] In another example, the dicrotic notch detection module can calculate or generate an AP derivative signal of the AP signal, deconstruct the AP derivative signal into derivative FC bins, select or generate a second subset of FC bins from the derivative FC bins, and reconstruct a derivative dicrotic notch signal based on the second subset of FC bins, wherein the detecting the dicrotic notch events is based in part on the derivative dicrotic notch signal. Start and end events along the AP signal can be identified based on peaks of at least one of negative or positive segments of i) the filtered AP signal and ii) the derivative dicrotic notch signal reconstructed from the second subset of FC bins. Identifying or generating the start and end events can include applying a threshold function to the filtered AP signal to create a pseudo systolic pressure signal, the threshold function applying a first value to positive elements of the filtered AP signal and a second value to negative elements of the filtered AP signal, identifying or generating peaks of the positive segment of the pseudo systolic pressure signal, and identifying or generating peaks of the negative segment of the derivative dicrotic notch signal, the start and end events identified based on the peaks of the positive segment of the pseudo systolic pressure signal and the negative segments of the derivative dicrotic notch signal.

[00203] In yet another example, the dicrotic notch detection module can apply at least one of linear or nonlinear detrending to the AP signal to remove at least one of bias, drift, baseline wandering or high frequency noise. The dicrotic notch detection module can calculate or generate a normalized variability based on the dicrotic notch events, and the arrhythmia analysis module can determine that an arrythmia is associated with the AP signal when the normalized variability exceeds a threshold. In some cases, the normalized variability is based on amplitudes of the dicrotic notch events and time between adjacent dicrotic notch events.

[00204] The arrhythmia analysis module 1034 is configured to analyze the cardiac activity (CA) signals over one or more cardiac beats to identify the existence of a candidate arrhythmia. The microcontroller 1020 and/or arrhythmia analysis module 1034 can declare a candidate arrhythmia episode (e.g., VT or VF arrhythmia) based on the CA signals. For example, the arrhythmia analysis module 1034 can declare arrythmias based on the NDNV being greater than a threshold, as discussed herein.

[00205] In some embodiments, the arrhythmia analysis module 1034 can include morphology detection to review and analyze one or more features of the morphology of cardiac signals. In other embodiments, the arrhythmia analysis module 1034 can compare CA signals and/or pressure signals to one or more templates (e.g., stored in memory 1060) associated with normal sinus rhythm. The arrhythmia analysis module 1034 can analyze the cardiac signals indicative of cardiac events that are sensed by electrodes located proximate to one or more atrial and/or ventricular sites.

[00206] The systolic peak analysis module 1035 can analyze the pressure signals acquired by the IPS 150 and/or CA signals from the IMD 100, for a predetermined amount of time such as 10 seconds, 18 seconds, 20 seconds, etc., to identify peaks within the pressure signals as discussed herein.

[00207] Also, the microcontroller 1020 further controls a shocking circuit 1080 by way of a control signal 1082. The shocking circuit 1080 generates shocking pulses that are applied to the heart of the patient to terminate the detected arrhythmia through various configurations such as less than a full shock strength of one or more electrode through full shock strength with two or more electrodes, etc. The shocking circuit 1080 can generate high-voltage and/or medium-voltage and the shocking electrodes, such as the electrodes as discussed in Figure 1 , can be configured to deliver high-voltage or medium-voltage shocks.

[00208] The IMD 100 further includes a first chamber pulse generator 1090 that generates stimulation pulses (e.g., ATP) for delivery by one or more electrodes coupled thereto. The pulse generator 1090 is controlled by the microcontroller 1020 via control signal 1092. The pulse generator 1090 is coupled to the select electrode(s) via the electrode configuration switch 1026, which includes multiple switches for connecting the desired electrodes to the appropriate I/O circuits, thereby facilitating electrode programmability.

[00209] In some embodiments, the output of a sensing circuit 1044 is connected to the microcontroller 1020 which, in turn, triggers or inhibits the pulse generator 1090 and shocking circuit 1080. The sensing circuit 1044 receives a control signal 1094 from the microcontroller 1020 for purposes of controlling the gain, threshold, polarization charge removal circuitry (not shown), and the timing of any blocking circuitry (not shown) coupled to the inputs of the sensing circuitry. [00210] The IMD 100 may include one or more physiological sensor 1070. For example, sensor 1070 may adjust pacing stimulation rate according to the exercise state of the patient, detect changes in cardiac output, changes in the physiological condition of the heart, or diurnal changes in activity (e.g., detecting sleep and wake states). In other cases, the sensor 1070 can obtain accelerometer data with respect to a global coordinate system that is defined relative to a gravitational direction that may be utilized to identify a posture of the patient, movement of the IMD 100 within the patient, etc. While shown as being included within the housing 1040, the physiological sensor 1070 may be external to the housing 1040, yet still, be implanted within or carried by the patient.

[00211] In still further embodiments, the physiological sensor 1070 may be the pressure sensor 150 and may be separate from or integrated with the IMD 100.

[00212] Although not shown, the microcontroller 1020 may further include other dedicated circuitry and/or firmware/software components that assist in monitoring various conditions of the patient's heart and managing pacing therapies.

[00213] A switch 1026 is optionally provided to allow selection of different electrode configurations under the control of the microcontroller 1020. The electrode configuration switch 1026 may include multiple switches for connecting the desired electrodes to the appropriate I/O circuits, thereby facilitating electrode programmability. The switch 1026 is controlled by a control signal 1028 from the microcontroller 1020. Optionally, the switch 1026 may be omitted and the I/O circuits directly connected to a housing electrode via terminal 1000 and one or more other electrodes via terminals 1002, 1004, 1006, 1008, 1010.

[00214] The IMD 100 is further equipped with a communication modem (modulator/demodulator) 1042 to enable wireless communication with other devices, implanted devices such as the IPS 150, and/or external devices 1054 (e.g., external device 104, wearable device 170). In one implementation, the communication modem 1042 uses high frequency modulation, for example using RF, Bluetooth or Bluetooth Low Energy telemetry protocols. The signals are transmitted in a high frequency range and will travel through the body tissue in fluids without stimulating the heart or being felt by the patient. The communication modem 1042 may be implemented in hardware as part of the microcontroller 1020, or as software/firmware instructions programmed into and executed by the microcontroller 1020. Alternatively, the modem 1042 may reside separately from the microcontroller as a standalone component. The modem 1042 facilitates data retrieval from a remote monitoring network. The modem 1042 enables timely and accurate data transfer directly from the patient to an electronic device utilized by a physician.

[00215] The IMD 100 includes the sensing circuit 1044 selectively coupled to one or more electrodes that perform sensing operations, through the switch 1026, to sense cardiac activity data/signals indicative of cardiac activity. The sensing circuit 1044 may include dedicated sense amplifiers, multiplexed amplifiers, or shared amplifiers. It may further employ one or more low power, precision amplifiers with programmable gain and/or automatic gain control, bandpass filtering, and threshold detection circuit to selectively sense the features of interest. In one embodiment, switch 1026 may be used to determine the sensing polarity of the cardiac signal by selectively closing the appropriate switches. The sensing circuit 1044 is configured to sense CA, on-demand and in real-time, for one or more cardiac cycles and generate one or more CA signals based on the CA.

[00216] In the example of Figure 10, a single sensing circuit 1044 is illustrated. Optionally, the IMD 100 may include multiple sensing circuits, similar to sensing circuit 1044, where each sensing circuit is coupled to two or more electrodes and controlled by the microcontroller 1020 to sense electrical activity detected at the corresponding two or more electrodes. The sensing circuit 1044 may operate in a unipolar sensing configuration or a bipolar sensing configuration. Optionally, the sensing circuit 1044 may be removed entirely, and the microcontroller 1020 perform the operations described herein based upon the CA signals from the A/D data acquisition system 1050 directly coupled to the electrodes. The output of the sensing circuit 1044 is connected to the microcontroller 1020 which, in turn, determines when to store the cardiac activity data of CA signals (digitized by the A/D data acquisition system 1050) in a memory 1060.

[00217] In some embodiments, the A/D data acquisition system 1050 is coupled to one or more electrodes via the switch 1026 to sample cardiac activity signals across any pair of desired electrodes.

[00218] A communications circuit 1064 can be utilized by the IMD 100 to send and receive communications and/or data between the IMD 100 and the external device 1054 through communications link 1065 and can utilize wireless communication protocols similar to I same as the communication modem 1042.

[00219] By way of example, the external device 1054 may represent a bedside monitor installed in a patient’s home and utilized to communicate with the IMD 100 while the patient is at home, in bed or asleep. The external device 1054 may be a programmer used in the clinic to interrogate the IMD 100, retrieve data and program detection criteria and other features. The external device 1054 may be a handheld device (e.g., smartphone, tablet device, laptop computer, smartwatch and the like) that may be coupled over a network (e.g., the Internet) to a remote monitoring service, medical network and the like. The external device 1054 can also act as a one-way and/or bidirectional bridge/gateway to convey messages, requests, and/or signals (e.g., CA signals, pressure signals, etc.) between the IMD 100 and the IPS 150. The external device 1054 can be the IPS 150. The external device 1054 may communicate with the communications circuit 1064 of the IMD 100 through the communication link 1065. The external device 1054 facilitates access by physicians to patient data as well as permitting the physician to review real-time CA signals and/or pressure signals as collected by the IMD 100 and/or IPS 150, as well as data associated with valid heartbeats, etc. [00220] The microcontroller 1020 is coupled to a memory 1060 by a suitable data/address bus 1062. The memory 1060 stores the CA signals and can also store pressure signals, templates, as well as markers and other data content associated with the acquired signals. The memory 1060 also stores program instructions for accomplishing the embodiments described herein. For example, program instructions 1037 for at least the arrhythmia analysis module 1034, the systolic peak analysis module 1035, and the dicrotic notch detection module 1036 can be stored.

[00221] A battery 1072 provides operating power to some or all of the components in the IMD 100. The battery 1072 is capable of operating at low current drains for long periods of time. The battery 1072 also desirably has a predictable discharge characteristic so that elective replacement time may be detected. As one example, the housing 1040 employs lithium/silver vanadium oxide batteries. The battery 1072 may afford various periods of longevity (e.g., three years or more of device monitoring). In alternate embodiments, the battery 1072 could be rechargeable. See, for example, U.S. Patent Number 7,294,108, titled “Cardiac event micro-recorder and method for implanting same”, the complete subject matter of which is hereby incorporated by reference.

[00222] The IMD 100 further includes an impedance measuring circuit 1074, which can be used for many things, including: lead impedance surveillance for proper lead positioning or dislodgement; detecting operable electrodes and automatically switching to an operable pair if dislodgement occurs; measuring thoracic impedance for determining shock thresholds; detecting when the device has been implanted; measuring stroke volume; and detecting the opening of heart valves; and so forth. The impedance measuring circuit 1074 is coupled to the switch 1026 so that any desired electrode may be used. [00223] Figure 11 illustrates a digital healthcare system 1100 implemented in accordance with embodiments herein. The system 1100 utilizes signals detected by an IMD and/or an IPS, implanted for example in a patient’s pulmonary artery and/or other vessel, to determine dicrotic notch events, arrythmia, valid/invalid heartbeats of a patient, etc. The healthcare system 1100 may include wearable devices that communicate with an IMD, IPS, external device, and/or a remote database. As a result, the healthcare system 1100 may monitor health parameters of a patient, including valid heartbeats, heart rate, HRV, cardiac output, locations of dicrotic notch events, changes in variance between dicrotic notch events over time, etc., and/or therapies applied utilizing the health parameters, and provide a diagnosis and/or recommendations for the patient based on the monitored health parameters, adjust treatment parameters, etc.

[00224] The system 1100 may be implemented with various architectures, that are collectively referred to as a healthcare system 1120. By way of example, the healthcare system 1120 may be implemented as described herein. The healthcare system 1120 may be a patient care network, such as the Merlin.net™ patient care network operated by Abbott Laboratories (headquartered in the Abbott Park Business Center in Lake Bluff, III.)

[00225] The healthcare system 1120 is configured to receive data, including IMD data from a variety of external and implantable sources including, but not limited to, active IMDs 1102 capable of delivering therapy to a patient, passive IMDs (e.g., cardiac monitors) or sensors 1104 (e.g., IPS), wearable devices/sensors 1108, and point-of-care (POC) devices 1110 (e.g., at home or at a medical facility). Any of the IMD 1102, sensor 1104, sensor 1108, and/or POC device 1110 may analyze a signal acquired for a period of time to determine the dicrotic notch and valid/invalid/arrhythmic heartbeats as described herein. The data from one or more of the external and/or implantable sources is collected and communicated to one or more secure databases within the healthcare system 1120. Optionally, the patient and/or other users may utilize a device, such as a smart phone, tablet device, etc., to enter data.

[00226] Figure 12 illustrates a computer-implemented method for detecting the location of the dicrotic notch in cardiac pressure signals in accordance with embodiments herein. The operations of Figure 12 may be implemented by hardware, firmware, circuitry and/or one or more processors housed partially and/or entirely within an IMD 100, an IPS 150, an external device 104, wearable device 170, a local external device, remote server or more generally within a health care system. Optionally, the operations of Figure 12 may be shared across devices, and thus partially implemented by one or more of an IMD 100, an IPS 150, an external device 104, a wearable device 170, a local external device, remote server or more generally within a health care system. For example, the IMD 100 includes IMD memory and one or more IMD processors, the IPS 150 includes IPS memory and one or more IPS processors, the external device 104 includes external device memory and one or more external device processors, and the wearable device 170 includes wearable memory and one or more wearable processors, and further, other of the external devices/systems (e.g., local, remote or anywhere within the health care system) that may implement the operations of Figure 12 include external device memory and one or more external device processors.

[00227] At 1202, one or more processors sense the AP signal. The AP signal is representative of variations in AP occurring during individual cardiac cycles within the patient.

[00228] At 1204, the one or more processors apply a wavelet transformation to the AP signal to decompose the AP signal into FC bins.

[00229] At 1206, the one or more processors select a subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest. [00230] At 1208, the one or more processors reconstruct the subset of FC bins to form a filtered AP signal.

[00231] At 1210, the one or more processors detect dicrotic notch events along the filtered AP signal.

[00232] Optionally, the AP signal can be sensed utilizing the AP sensor.

[00233] Optionally, the one or more processors can monitor a hemodynamic condition of the patient based on the dicrotic notch events.

[00234] Example 1. A method for use with an implantable arterial pressure sensor. The method comprises sensing an arterial pressure (AP) signal utilizing the AP sensor, the AP signal representative of variations in AP occurring during individual cardiac cycles within the patient; applying a wavelet transformation to the AP signal to decompose the AP signal into frequency component (FC) bins; selecting a subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest; reconstructing the subset of FC bins to form a filtered AP signal; and detecting dicrotic notch events along the filtered AP signal. Optionally, Example 1 can include monitoring a hemodynamic condition of the patient based on the dicrotic notch events.

[00235] Example 2. The method of example 1 , wherein the FC bins separate frequency components that correspond to different aspects of the cardiac cycle and different interactions between the heart and pulmonary circulation.

[00236] Example 3. The method of examples 1 or 2, wherein the subset of the FC bins selected includes frequency components associated with movement of at least one of the aortic or pulmonary valves.

[00237] Example 4. The method of any one of examples 1 to 3, further comprising: calculating a normalized variability based on the dicrotic notch events; and determining that an arrythmia is associated with the AP signal when the normalized variability exceeds a threshold. [00238] Example 5. The method of any one of examples 1 to 4, wherein each of the FC bins including an AP signal for a select FC over a select time frame.

[00239] Example 6. The method of any one of examples 1 to 5, wherein the wavelet transformation is a time domain based transformation.

[00240] Example 7. The method of any one of examples 1 to 6, further comprising: calculating an AP derivative signal of the AP signal; deconstructing the AP derivative signal into derivative FC bins; selecting a second subset of FC bins from the derivative FC bins; and reconstructing a derivative dicrotic notch signal based on the second subset of FC bins, wherein the detecting the dicrotic notch events is based in part on the derivative dicrotic notch signal.

[00241] Example 8. The method of example 7, further comprising identifying start and end events along the AP signal based on peaks of at least one of negative or positive segments of i) the filtered AP signal and ii) the derivative dicrotic notch signal reconstructed from the second subset of FC bins.

[00242] Example 9. The method of example 8, wherein the identifying the start and end events further comprises: applying a threshold function to the filtered AP signal to create a pseudo systolic pressure signal, the threshold function applying a first value to positive elements of the filtered AP signal and a second value to negative elements of the filtered AP signal; identifying peaks of the positive segment of the pseudo systolic pressure signal; and identifying peaks of the negative segment of the derivative dicrotic notch signal, the start and end events identified based on the peaks of the positive segment of the pseudo systolic pressure signal and the negative segments of the derivative dicrotic notch signal. [00243] Example 10. The method of any one of examples 1-9, further comprising applying at least one of linear or nonlinear detrending to the AP signal to remove at least one of bias, drift, baseline wandering or high frequency noise.

[00244] Example 11. A system for determining dicrotic notch events within an arterial pressure signal, comprising: an external device; and an implantable pressure sensor (IPS) comprising: an IPS sensing circuit configured to sense pressure for a period of time, and to generate a pressure signal based on the pressure; and an IPS communications circuit configured to communicate with the external device; wherein at least one of the IPS or external device further comprises: memory configured to store program instructions; and one or more processors that, when executing the program instructions, are configured to: sense an arterial pressure (AP) signal utilizing the IPS sensor, the AP signal representative of variations in AP occurring during individual cardiac cycles within the patient; apply a wavelet transformation to the AP signal to decompose the AP signal into frequency component (FC) bins; select a subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest; reconstruct the subset of FC bins to form a filtered AP signal; and detect dicrotic notch events along the filtered AP signal. Optionally, example 11 can also monitor a hemodynamic condition of the patient based on the dicrotic notch events.

[00245] Example 12. The system of example 11 , wherein the FC bins separate frequency components that correspond to different aspects of the cardiac cycle and different interactions between the heart and pulmonary circulation.

[00246] Example 13. The system of examples 11 or 12, wherein the subset of the FC bins selected includes frequency components associated with movement of at least one of the aortic or pulmonary valves.

[00247] Example 14. The system of any one of examples 11 to 13, wherein the wavelet transformation is a time domain based transformation.

[00248] Example 15. The system of any one of examples 11 to 14, wherein the one or more processors are further configured to: calculate a normalized variability based on the dicrotic notch events; and determine that an arrythmia is associated with the AP signal when the normalized variability exceeds a threshold. [00249] Example 16. The system of any one of examples 11 to 15, wherein the normalized variability is based on amplitudes of the dicrotic notch events and time between adjacent dicrotic notch events. [00250] Example 17. The system of any one of examples 11 to 16, wherein the one or more processors are further configured to: calculate an AP derivative signal of the AP signal; deconstruct the AP derivative signal into derivative FC bins; select a second subset of FC bins from the derivative FC bins; and reconstruct a derivative dicrotic notch signal based on the second subset of FC bins, wherein the detecting the dicrotic notch events is based in part on the derivative dicrotic notch signal.

[00251] Example 18. The system of any one of examples 11 to 17, wherein the one or more processors are further configured to identify start and end events along the AP signal based on peaks of at least one of negative or positive segments of i) the filtered AP signal and ii) the derivative dicrotic notch signal reconstructed from the second subset of FC bins.

[00252] Example 19. The system of example 18, wherein the one or more processors are further configured to: apply a threshold function to the filtered AP signal to create a pseudo systolic pressure signal, the threshold function applying a first value to positive elements of the filtered AP signal and a second value to negative elements of the filtered AP signal; identify peaks of the positive segment of the pseudo systolic pressure signal; and identify peaks of the negative segment of the derivative dicrotic notch signal, the start and end events identified based on the peaks of the positive segment of the pseudo systolic pressure signal and the negative segments of the derivative dicrotic notch signal.

Example 20. The system of any one of examples 11 to 19, wherein the one or more processors are further configured to apply at least one of linear or nonlinear detrending to the AP signal to remove at least one of bias, drift, baseline wandering or high frequency noise.

Closing

[00253] It should be clearly understood that the various arrangements and processes broadly described and illustrated with respect to the Figures, and/or one or more individual components or elements of such arrangements and/or one or more process operations associated of such processes, can be employed independently from or together with one or more other components, elements and/or process operations described and illustrated herein. Accordingly, while various arrangements and processes are broadly contemplated, described and illustrated herein, it should be understood that they are provided merely in illustrative and non-restrictive fashion, and furthermore can be regarded as but mere examples of possible working environments in which one or more arrangements or processes may function or operate.

[00254] As will be appreciated by one skilled in the art, various aspects may be embodied as a system, method or computer (device) program product. Accordingly, aspects may take the form of an entirely hardware embodiment or an embodiment including hardware and software that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects may take the form of a computer (device) program product embodied in one or more computer (device) readable storage medium(s) having computer (device) readable program code embodied thereon.

[00255] Any combination of one or more non-signal computer (device) readable media may be utilized. The non-signal media may be a storage medium. A storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a storage medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a dynamic random access memory (DRAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. [00256] Program code for carrying out operations may be written in any combination of one or more programming languages. The program code may execute entirely on a single device, partly on a single device, as a stand-alone software package, partly on single device and partly on another device, or entirely on the other device. In some cases, the devices may be connected through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made through other devices (for example, through the Internet using an Internet Service Provider) or through a hard wire connection, such as over a USB connection. For example, a server having a first processor, a network interface, and a storage device for storing code may store the program code for carrying out the operations and provide this code through its network interface via a network to a second device having a second processor for execution of the code on the second device.

[00257] Aspects are described herein with reference to the figures, which illustrate example methods, devices and program products according to various example embodiments. The program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing device or information handling device to produce a machine, such that the instructions, which execute via a processor of the device implement the functions/acts specified. The program instructions may also be stored in a device readable medium that can direct a device to function in a particular manner, such that the instructions stored in the device readable medium produce an article of manufacture including instructions which implement the function/act specified. The program instructions may also be loaded onto a device to cause a series of operational steps to be performed on the device to produce a device implemented process such that the instructions which execute on the device provide processes for implementing the functions/acts specified.

[00258] The units/modules/applications herein may include any processorbased or microprocessor-based system including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), logic circuits, and any other circuit or processor capable of executing the functions described herein. Additionally, or alternatively, the modules/controllers herein may represent circuit modules that may be implemented as hardware with associated instructions (for example, software stored on a tangible and non-transitory computer readable storage medium, such as a computer hard drive, ROM, RAM, or the like) that perform the operations described herein. The above examples are exemplary only, and are thus not intended to limit in any way the definition and/or meaning of the term “controller.” The units/modules/applications herein may execute a set of instructions that are stored in one or more storage elements, in order to process data. The storage elements may also store data or other information as desired or needed. The storage element may be in the form of an information source or a physical memory element within the modules/controllers herein. The set of instructions may include various commands that instruct the modules/applications herein to perform specific operations such as the methods and processes of the various embodiments of the subject matter described herein. The set of instructions may be in the form of a software program. The software may be in various forms such as system software or application software. Further, the software may be in the form of a collection of separate programs or modules, a program module within a larger program or a portion of a program module. The software also may include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to user commands, or in response to results of previous processing, or in response to a request made by another processing machine.

[00259] It is to be understood that the subject matter described herein is not limited in its application to the details of construction and the arrangement of components set forth in the description herein or illustrated in the drawings hereof. The subject matter described herein is capable of other embodiments and of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[00260] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings herein without departing from its scope. While the dimensions, types of materials and coatings described herein are intended to define various parameters, they are by no means limiting and are illustrative in nature. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the embodiments should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms "including" and "in which" are used as the plain-English equivalents of the respective terms "comprising" and "wherein." Moreover, in the following claims, the terms "first," "second," and "third," etc., are used merely as labels, and are not intended to impose numerical requirements on their objects or order of execution on their acts.

Claims

WHAT IS CLAIMED IS:

1. A method for use with an implantable arterial pressure sensor, the method comprising: sensing an arterial pressure (AP) signal utilizing the AP sensor, the AP signal representative of variations in AP occurring during individual cardiac cycles within the patient; applying a wavelet transformation to the AP signal to decompose the AP signal into frequency component (FC) bins; selecting a subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest; reconstructing the subset of FC bins to form a filtered AP signal; detecting dicrotic notch events along the filtered AP signal; and monitoring a hemodynamic condition of the patient based on the dicrotic notch events.

2. The method of claim 1 , wherein the FC bins separate frequency components that correspond to different aspects of the cardiac cycle and different interactions between the heart and pulmonary circulation.

3. The method of claim 1 , wherein the subset of the FC bins selected includes frequency components associated with movement of at least one of the aortic or pulmonary valves.

4. The method of claim 1 , further comprising: calculating a normalized variability based on the dicrotic notch events; and determining that an arrythmia is associated with the AP signal when the normalized variability exceeds a threshold.

5. The method of claim 1 , wherein each of the FC bins including an AP signal for a select FC over a select time frame.

6. The method of claim 1 , wherein the wavelet transformation is a time domain based transformation.

7. The method of claim 1 , further comprising: calculating an AP derivative signal of the AP signal; deconstructing the AP derivative signal into derivative FC bins; selecting a second subset of FC bins from the derivative FC bins; and reconstructing a derivative dicrotic notch signal based on the second subset of FC bins, wherein the detecting the dicrotic notch events is based in part on the derivative dicrotic notch signal.

8. The method of claim 7, further comprising identifying start and end events along the AP signal based on peaks of at least one of negative or positive segments of i) the filtered AP signal and ii) the derivative dicrotic notch signal reconstructed from the second subset of FC bins.

9. The method of claim 8, wherein the identifying the start and end events further comprises: applying a threshold function to the filtered AP signal to create a pseudo systolic pressure signal, the threshold function applying a first value to positive elements of the filtered AP signal and a second value to negative elements of the filtered AP signal; identifying peaks of the positive segment of the pseudo systolic pressure signal; and identifying peaks of the negative segment of the derivative dicrotic notch signal, the start and end events identified based on the peaks of the positive segment of the pseudo systolic pressure signal and the negative segments of the derivative dicrotic notch signal.

10. The method of claim 1 , further comprising applying at least one of linear or nonlinear detrending to the AP signal to remove at least one of bias, drift, baseline wandering or high frequency noise.

11. A system for determining dicrotic notch events within an arterial pressure signal, comprising: an external device; and an implantable pressure sensor (IPS) comprising: an IPS sensing circuit configured to sense pressure for a period of time, and to generate a pressure signal based on the pressure; and an IPS communications circuit configured to communicate with the external device; wherein at least one of the IPS or external device further comprises: memory configured to store program instructions; and one or more processors that, when executing the program instructions, are configured to: sense an arterial pressure (AP) signal utilizing the IPS sensor, the AP signal representative of variations in AP occurring during individual cardiac cycles within the patient; apply a wavelet transformation to the AP signal to decompose the AP signal into frequency component (FC) bins; select a subset of the FC bins having frequency components associated with a dicrotic notch indicative of closure of at least one of a valve of interest; reconstruct the subset of FC bins to form a filtered AP signal; detect dicrotic notch events along the filtered AP signal; and monitor a hemodynamic condition of the patient based on the dicrotic notch events.

12. The system of claim 11 , wherein the FC bins separate frequency components that correspond to different aspects of the cardiac cycle and different interactions between the heart and pulmonary circulation.

13. The system of claim 11 , wherein the subset of the FC bins selected includes frequency components associated with movement of at least one of the aortic or pulmonary valves.

14. The system of claim 11 , wherein the wavelet transformation is a time domain based transformation.

15. The system of claim 11 , wherein the one or more processors are further configured to: calculate a normalized variability based on the dicrotic notch events; and determine that an arrythmia is associated with the AP signal when the normalized variability exceeds a threshold.

16. The system of claim 15, wherein the normalized variability is based on amplitudes of the dicrotic notch events and time between adjacent dicrotic notch events.

17. The system of claim 11 , wherein the one or more processors are further configured to: calculate an AP derivative signal of the AP signal; deconstruct the AP derivative signal into derivative FC bins; select a second subset of FC bins from the derivative FC bins; and reconstruct a derivative dicrotic notch signal based on the second subset of FC bins, wherein the detecting the dicrotic notch events is based in part on the derivative dicrotic notch signal.

18. The system of claim 17, wherein the one or more processors are further configured to identify start and end events along the AP signal based on peaks of at least one of negative or positive segments of i) the filtered AP signal and ii) the derivative dicrotic notch signal reconstructed from the second subset of FC bins.

19. The system of claim 18, wherein the one or more processors are further configured to: apply a threshold function to the filtered AP signal to create a pseudo systolic pressure signal, the threshold function applying a first value to positive elements of the filtered AP signal and a second value to negative elements of the filtered AP signal; identify peaks of the positive segment of the pseudo systolic pressure signal; and identify peaks of the negative segment of the derivative dicrotic notch signal, the start and end events identified based on the peaks of the positive segment of the pseudo systolic pressure signal and the negative segments of the derivative dicrotic notch signal.

20. The system of claim 11 , wherein the one or more processors are further configured to apply at least one of linear or nonlinear detrending to the AP signal to remove at least one of bias, drift, baseline wandering or high frequency noise.