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WO2010000009A1 - Détection améliorée d'un dysfonctionnement cardiaque - Google Patents

Détection améliorée d'un dysfonctionnement cardiaque Download PDF

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Publication number
WO2010000009A1
WO2010000009A1 PCT/AU2008/000972 AU2008000972W WO2010000009A1 WO 2010000009 A1 WO2010000009 A1 WO 2010000009A1 AU 2008000972 W AU2008000972 W AU 2008000972W WO 2010000009 A1 WO2010000009 A1 WO 2010000009A1
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WO
WIPO (PCT)
Prior art keywords
ecg
data
subsequence
heart function
patient
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/AU2008/000972
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English (en)
Inventor
Aaron Ginzburg
Andrew Maxwell Tonkin
Alexander Tsintsiper
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
CARDANAL Pty Ltd
Original Assignee
CARDANAL Pty Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by CARDANAL Pty Ltd filed Critical CARDANAL Pty Ltd
Priority to PCT/AU2008/000972 priority Critical patent/WO2010000009A1/fr
Publication of WO2010000009A1 publication Critical patent/WO2010000009A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms

Definitions

  • a method and system for processing ECG signals in order to identify possible abnormality of heart function of a patient is disclosed in commonly assigned US patent application serial no. 10/865,985, filed on 6 November 2004, and published under publication no. US 2005/0027202 on 2 March 2005.
  • data is extracted from an ECG signal of the patient, and a time derivative of the data is determined.
  • a normalised index value, representative of energy in the time derivative, is then generated.
  • the normalised index value computed in accordance with this prior art method has been shown to provide a useful indication of possible abnormality of heart function of the patient, the prior art methods and systems suffer from a number of limitations. In particular, it has previously been considered necessary to monitor the patient over an extended period of time. Specifically, the normalised index value computed in accordance with the above-described method provides only a relative indication of patient health. That is, a "normal" value of the index for a particular individual may be very different from a corresponding "normal” value for a different individual. It was accordingly not considered possible to identify potential problems based upon a single period of monitoring of a patient's ECG waveform.
  • the normalised index was proposed to be used as part of an ongoing monitoring process of the patient, wherein ECG measurements of the patient would be recorded at different times, enabling a historical average of the value of the normalised index value to be computed for each individual patient. Under these conditions, it was demonstrated in animal trials, and in clinical trials including comparative tests relative to the diagnosis of expert cardiologists, that the normalised index value is a useful and potentially highly sensitive indicator of abnormality of heart function, detectable by a deviation, in a subsequent measurement, of the index value from the historical average.
  • the present invention provides a method of measuring heart function of a patient comprising the steps of: performing an electrocardiogram (ECG) measurement of the patient over a continuous time interval having a specified duration, to obtain a sequence of ECG data corresponding with an ECG signal over said time interval; dividing the sequence of ECG data into a plurality of subsequences; processing each subsequence of ECG data to obtain a corresponding plurality of subsequence index values; computing a statistical mean value and a statistical variation value of the plurality of subsequence index values; and using said statistical mean value and said statistical variation value to compute a heart function index value which is a measure of heart function of the patient, wherein said processing step comprises, for each subsequence of ECG data: extracting data corresponding with specified ECG signal segments from the subsequence of ECG data; determining a time derivative of said extracted data; and generating a subsequence index value which is representative of energy
  • embodiments of the present invention enable a preliminary assessment of possible abnormality of heart function of a patient based upon a single ECG measurement.
  • the heart function index value computed in accordance with the aforementioned method, may be immediately indicative of potential problems, and a past history of patient monitoring is not necessary in order to identify any change. Accordingly, while the method does not provide a diagnosis of the cause of any identified abnormality, and therefore does not replace the expertise of a qualified cardiologist, it has the advantage that it may be implemented in an entirely automated system, such that the necessary measurements may be conducted by health care professionals, such as general practitioners or nurses, who do not have particular expertise in cardiology.
  • the method additionally includes performing automated quality control by excluding from further computations data corresponding with beats of the ECG signal that do not satisfy specified quality criteria.
  • One preferred form of quality control includes computing a noise value and a signal-to- noise ratio for each specified signal segment, and excluding from further computations any segment for which the signal-to-noise ratio is less than a predetermined value.
  • An additional preferred form of quality control includes determining the number of beats or complexes successfully identified for each ECG lead, and excluding from further computations data corresponding with any lead for which the number of successfully identified beats or complexes is less than a predetermined value.
  • Figures 2A and 2B are flowcharts illustrating a method of detecting possible abnormality of heart function of a patient according to a preferred embodiment of the invention
  • Figures 3A and 3B are schematic representations of ECG waveforms corresponding with typical heartbeats, illustrating methods of identifying specified points and/or intervals of the ECG signals, according to a preferred embodiment of the invention
  • the server computer 108 further includes an additional storage medium 114, typically being a suitable type of volatile memory, such as random access memory, for containing program instructions and transient data relating to the operation of the computer 108. Additionally, the computer 108 includes a network interface 116, accessible to the central processor 110, facilitating communications via the Internet 102 with other devices, such as electrocardiograph 104 and user computer 106.
  • an additional storage medium 114 typically being a suitable type of volatile memory, such as random access memory, for containing program instructions and transient data relating to the operation of the computer 108.
  • the computer 108 includes a network interface 116, accessible to the central processor 110, facilitating communications via the Internet 102 with other devices, such as electrocardiograph 104 and user computer 106.
  • the memory device 114 contains a body of program instructions 118 implementing various software-implemented features of the present invention, as are described in greater detail below, with reference to the remaining drawings. In general, these features include analysis and processing functions, for detecting possible abnormality of heart function of a patient, as described with reference to Figures 2 to 4. Additionally, a database server application is implemented, which enables patient records held in a database on the storage device 112 to be accessed remotely, for example from user computer 106.
  • the database server application may include commercially available and/or open source database systems, or a proprietary database system, and may be accessed either via a dedicated client application (such as is described below with reference to Figures 5 to 10), or alternatively a Web-based interface may be provided.
  • the output of the electrocardiograph 104 which is provided to the method at step 202, consists in all of 12 sequences of data values, each including 120,000 data points, representing 2 minutes of measurements at 1 ,000 Hz. If desired, a lesser or greater number of leads may be utilised, a different sampling rate may be employed, and/or the patient may be monitored over a shorter or longer time interval. It is presently believed, however, that a measurement interval of at least 2 minutes is desirable, in order to obtain sufficient data for subsequent processing. Longer intervals may provide additional information, such as intervals of 3 minutes, 4 minutes, or 5 minutes.
  • the ECG waveform data is segmented, by dividing the sequence of ECG data into a plurality of subsequences.
  • the overall ECG waveform for each of the leads utilised in the measurement 202 is divided into 12 segments, each of which therefore corresponds with 10 seconds of measurement in the preferred case, or equivalently 10,000 sample values.
  • each subsequence of ECG values, on each of the leads utilised in the measurement is processed to obtain corresponding subsequence index values. The method by which the subsequence index values are calculated will now be described, with reference to the flow chart shown in Figure 2B.
  • the first step in the process 206 is to extract data corresponding with specified ECG signal segments, at step 212.
  • the methods by which the ECG signal segments of interest are identified, such that the data may be extracted, are described in greater detail below with reference to Figures 3A and 3B.
  • the result of the extraction is a sequence of consecutive samples of the ECG data corresponding with a particular segment or interval of the ECG waveform. It is also particularly to be noted that this extraction is performed for each "beat" that is identifiable within the ECG signal subsequences, utilising the methods described below. That is, there is no averaging performed over multiple beats, and all of the available data is utilised in the analysis.
  • an overall heart function index value is computed for each lead.
  • the heart function index value is based upon the statistical mean value and the statistical variation value previously computed for each lead, and in presently preferred embodiments consists of the ratio of the statistical variation value (ie standard deviation) divided by the statistical mean value (Ze the average). This quantity is commonly known as the "coefficient of variation”. This is a quantity which has a smaller value when there is less variation amongst the subsequence index values computed in each of the subsequences of the ECG data on a particular lead. It has been discovered by the present inventors that this is a very sensitive and effective measure of possible abnormality of heart function.
  • a particular threshold may be established at which it is recommended that the patient return within a specific interval of time, such as six months, for a follow-up test.
  • the recommendation may be that the patient be referred, at the earliest convenient time, to a cardiologist for further analysis, diagnosis and/or treatment.
  • a heart function index value is made available corresponding with each lead.
  • up to 12 heart function index values are provided, corresponding with the 12 standard leads utilised in typical ECG measurement.
  • the index values may be utilised in a number of ways, as will now be described.
  • each individual heart function index value may be considered separately. It has been found that, in the case of some patients, the heart function index value may be relatively low for a majority of leads, but may be relatively high for one or more individual leads. Thus, though an average of the heart function index value may be low over all leads, an abnormality of heart function relating to the measurement performed by a particular lead having a high index value may be indicated. Accordingly, the provision of individual heart function index values corresponding with each individual lead to a practitioner operating the test, or analysing the results, may be advantageous.
  • a global heart function index value may be calculated, being an average of the heart function index values corresponding with each individual ECG lead.
  • the global heart function index value will provide a strong overall indication of patient health, however as noted above there may be some cases in which this value is relatively low despite one or more individual leads providing indications of possible abnormality. It may generally be concluded from this, however, that a relatively high value of the global heart function index value is a particularly strong indication of a potential problem.
  • one or more cluster heart function index values may be computed, corresponding with selected groups of ECG leads. That is, additional index values may be calculated which correspond with specific lead clusters.
  • any meaningful or interesting cluster of leads may be selected, however there are particular clusters of leads that are established as having an association with particular regions of the heart, and the detection of corresponding abnormalities of function.
  • the standard leads II, III and aVF known as the "inferior group”
  • the cluster of leads v1 to v6, known as the "anterior group” are of particular interest.
  • a corresponding "regional heart function index” may be computed as an average of the heart function index values corresponding with each ECG lead of the cluster.
  • review of the cluster/regional heart function index values may enable abnormalities associated with corresponding regions of the heart to be identified, even if the overall (global) heart function index is relatively low.
  • FIG. 3A and 3B there is shown schematic representations of ECG waveforms corresponding with typical heartbeats. Methods of identifying specified points and/or intervals of the ECG signal, enabling data corresponding with specified ECG signal segments to be automatically extracted, will now be described with reference to these drawings.
  • Figure 3A represents a schematic representation 300 of a single beat of a conventional ECG waveform.
  • the most commonly observed features are represented, in an idealised form, within the waveform 300.
  • P P
  • QRS complex including the Q-, R- and S-peaks (labelled accordingly) and nominally terminating at the point labelled J.
  • J T
  • T T-wave
  • FIG. 3B two adjacent beats 302, 304 are shown. Nominal demarcation lines, 306, 308 are also depicted, which represent, respectively, a time shortly following the QRS complex of beat 302, and a time shortly prior to the QRS complex of beat 204. Appropriate time values 306, 308 may be identified once the QRS complexes have been identified, in the manner shortly to be described.
  • the general process of feature identification employed in preferred embodiments of the invention proceeds as follows. Firstly, the ECG signal is filtered or smoothed in order to reduce noise. In a particularly preferred embodiment, a least squares cubic spline fit is performed in order to generate a smoothed ECG signal waveform. Further processing, as now described, is then conducted using the noise-reduced spline fit. Firstly, one or more time derivatives of the spline fit are computed. In the presently preferred embodiments, first and second time derivatives are computed. The Q-peak, R-peak, and S-peak are then determined, by identifying within the first and second derivatives those points having the highest values. As can be seen in the schematic diagram 300, the Q-, R- and S-peaks exhibit the greatest rate of change in all derivatives of the ECG waveform.
  • the J-point is identified, by searching the time derivative data for the point exhibiting the largest curvature following the identified S-peak.
  • the location of the QRS complex within the beat 300, and indeed within adjacent beats 302, 304 is known.
  • the points 306, 308 may then be determined, and a search for the T- and P-waves appearing between these time instants is then conducted.
  • the T-wave and P-wave are identified by points of highest curvature, corresponding with the start and/or end points of each wave, lying between the points 306 and 308.
  • the extended ST-segment may include the portion of the waveform 300 within a 30 ms interval prior to the J-point.
  • This interval may include a so-called "late potential" of the QRS complex, which is known by cardiologists to be significant in the assessment of patient heart function.
  • Other interval lengths prior to the J-point such as 10 ms, or 20 ms, may also be utilised, and in a preferred embodiment the extended interval may be specified by the user.
  • each recorded beat 300 may be utilised in the calculation of the heart function index values, presently favoured embodiments of the invention preferably analyse the QRS complex and/or the ST-segment.
  • Other sub-intervals of the waveform 300 are being studied, in relation to their effectiveness in producing heart function index values that are reliable and sensitive indicators of potential abnormality. It is therefore envisaged that any particularly advantageous intervals that are subsequently identified will be readily incorporated into embodiments of the invention.
  • a further feature of presently preferred embodiments is the use of quality control techniques, in order to exclude from the calculations any data corresponding with beats of the ECG signals that do not satisfy specified quality criteria. It should be appreciated that factors such as noise, interference, patient movement, and various other external factors, may result in some portions of the ECG waveforms being very unreliable indicators of underlying heart function. It is therefore desirable that any such portions of the waveforms be excluded from critical calculations.
  • Two possible methods of quality control, employed in preferred embodiments of the invention, are illustrated by the flowcharts in Figures 4A and 4B.
  • the flowchart 400 of Figure 4A is illustrative of a method based on a signal-to-noise ratio (SNR) calculation
  • the flowchart 412 in Figure 4B is illustrative of a method based upon a check of likely quality of data on individual ECG leads.
  • a noise value is calculated associated with an individual beat, and a corresponding SNR value. More specifically, noise is calculated based upon the "flat" regions of the waveform, such as those intervals prior to the P-wave, or following the T-wave, as illustrated in Figure 3A. Noise is determined using the difference between the raw data and the noise-reduced spline fit, described previously in relation to Figure 3A. A signal value is estimated as a peak-to-peak amplitude, ie between the Q- and/or S-peaks, and the R-peak at the opposing extreme.
  • the SNR value is a ratio of the aforementioned signal amplitude to the aforementioned noise measure.
  • a comparison is performed between the computed SNR and an acceptable SNR minimum threshold. The value of this threshold is selected based upon experience so as to reject a majority of beats that would be considered to be excessively noisy. This value may potentially be made available, in some embodiments, as a parameter that can be varied by the user. If the SNR does not exceed the minimum threshold, then the beat is rejected at step 408, and data is not extracted for inclusion in subsequent computations. However, if the SNR exceeds the minimum threshold, then the beat is accepted at step 406, and is utilised in subsequent calculations.
  • the flowchart 412 illustrates a method employed to assess the quality of information available on individual ECG leads. More particularly, it will be appreciated from the foregoing description that through the process of beat identification, discussed with reference to Figures 3A and 3B, and beat rejection, discussed with reference to Figure 4A, there will subsequently be a known number of identified and usable beats detected over the full measurement interval (eg 120 seconds) on each ECG lead. At step 414 this number of identified and usable beats is counted for a particular lead.
  • the counted value is compared with a minimum threshold. For example, it might be expected for a patient having a heart rate of 80 beats-per-minute, measured over a 2-minute interval, that approximately 160 beats should be recorded. However, if on a particular lead the actual counted number of usable beats is substantially less than this value, it is likely that the lead is particularly badly affected by noise, or other factors, and is therefore of questionable validity. Additionally, if the number of identifiable and usable beats is too low, the statistical significance of the final calculated heart function index value may be doubtful. Accordingly, if the number of counted beats is too low, then the entire lead is rejected at step 420. Otherwise the lead is accepted at step 418.
  • Figure 5 is a screen display 500 illustrating a window of a particular database client application, executing on the user computer 106.
  • the window includes a patient search form 502, corresponding patient search results 504, a list of corresponding patient consultation records held within the database, shown in the region 506, and information associated with a selected consultation in region 508.
  • a button 512 is provided enabling the user to view a corresponding stored ECG record.
  • the server system 108 employs a user identification and authentication system, whereby each user of the system, utilising the client application from a computer, eg 106, is required to log in using at least a user identifier (such as a user name, email address or the like), and a password.
  • a user identifier such as a user name, email address or the like
  • Different levels of access may be provided to data within the database according to an associated level of user authorisation.
  • a user may be a doctor associated with a particular clinic, and will thus be provided only with access to records corresponding with patients of that clinic.
  • the user may be a doctor associated with particular patients, regardless of clinic, and will be provided with access to the records of those patients.
  • this provides patients with greater mobility, while enabling doctors and other health care professionals associated with the patients to gain access to relevant records at any appropriate time and place.
  • the windows may similarly be viewed side-by-side, rather than one above the other. This feature enables a rapid comparison of differences in recorded data for the same patient at different times.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Cardiology (AREA)
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  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
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  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

L'invention porte sur un procédé de mesure de la fonction cardiaque d'un patient qui comprend l'étape de réalisation d'une mesure d'électrocardiogramme (ECG) du patient sur un intervalle de temps continu de durée spécifiée, afin d'obtenir une séquence correspondante de données d'ECG. La séquence de données d'ECG est divisée en une pluralité de sous-séquences, dont chacune est traitée pour obtenir une pluralité correspondante de valeurs d'indice de sous-séquence. En particulier, le traitement comprend l'extraction de données correspondant à des segments spécifiés des signaux d'ECG, la détermination d'une dérivée par rapport au temps des données extraites et la génération d'une valeur d'indice de sous-séquence qui est représentative de l'énergie dans ladite dérivée par rapport au temps. On calcule une valeur moyenne statistique et une valeur de variation statistique de la pluralité de valeurs d'indice de sous-séquence et on les utilise pour calculer une valeur d'indice de fonction cardiaque qui est une mesure de la fonction cardiaque du patient. L'invention porte également sur des systèmes et sur un appareil mettant en œuvre le procédé. L'invention présente l'avantage de permettre une évaluation préliminaire d'une anomalie possible de fonction cardiaque d'un patient en se basant sur une mesure d'ECG unique.
PCT/AU2008/000972 2008-07-02 2008-07-02 Détection améliorée d'un dysfonctionnement cardiaque Ceased WO2010000009A1 (fr)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9255836B2 (en) 2010-06-17 2016-02-09 Weatherford Technology Holdings, Llc Fiber optic cable for distributed acoustic sensing with increased acoustic sensitivity
US9700226B2 (en) 2015-09-30 2017-07-11 Heart Test Laboratories, Inc. Quantitative heart testing
CN113288169A (zh) * 2021-05-26 2021-08-24 东软集团股份有限公司 一种心电波形信号的波形识别方法、装置及设备
CN113712568A (zh) * 2020-05-12 2021-11-30 深圳市科瑞康实业有限公司 一种心电信号数据的分类方法和装置
CN118177830A (zh) * 2024-05-15 2024-06-14 济南宝林信息技术有限公司 基于人工智能的心脏功能实时监听数据优化处理方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0448196A2 (fr) * 1990-03-19 1991-09-25 Del Mar Avionics Méthode et appareil d'analyse spectrale de signaux électrocardiographiques
US20050027202A1 (en) * 2001-11-28 2005-02-03 Aaron Ginzburg Method and system for processing electrocardial signals
US20050234363A1 (en) * 2004-04-15 2005-10-20 Ge Medical Systems Information Technologies, Inc. Method and apparatus for determining alternans data of an ECG signal
EP1790285A1 (fr) * 2004-05-20 2007-05-30 Kiyoshi Takizawa Méthode de diagnostic et son appareil et programme de diagnostic

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0448196A2 (fr) * 1990-03-19 1991-09-25 Del Mar Avionics Méthode et appareil d'analyse spectrale de signaux électrocardiographiques
US20050027202A1 (en) * 2001-11-28 2005-02-03 Aaron Ginzburg Method and system for processing electrocardial signals
US20050234363A1 (en) * 2004-04-15 2005-10-20 Ge Medical Systems Information Technologies, Inc. Method and apparatus for determining alternans data of an ECG signal
EP1790285A1 (fr) * 2004-05-20 2007-05-30 Kiyoshi Takizawa Méthode de diagnostic et son appareil et programme de diagnostic

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9255836B2 (en) 2010-06-17 2016-02-09 Weatherford Technology Holdings, Llc Fiber optic cable for distributed acoustic sensing with increased acoustic sensitivity
US9700226B2 (en) 2015-09-30 2017-07-11 Heart Test Laboratories, Inc. Quantitative heart testing
US10561327B2 (en) 2015-09-30 2020-02-18 Heart Test Laboratories, Inc. Quantitative heart testing
US11445968B2 (en) 2015-09-30 2022-09-20 Heart Test Laboratories, Inc. Quantitative heart testing
CN113712568A (zh) * 2020-05-12 2021-11-30 深圳市科瑞康实业有限公司 一种心电信号数据的分类方法和装置
CN113712568B (zh) * 2020-05-12 2024-02-09 深圳市科瑞康实业有限公司 一种心电信号数据的分类方法和装置
CN113288169A (zh) * 2021-05-26 2021-08-24 东软集团股份有限公司 一种心电波形信号的波形识别方法、装置及设备
CN113288169B (zh) * 2021-05-26 2024-05-17 东软集团股份有限公司 一种心电波形信号的波形识别方法、装置及设备
CN118177830A (zh) * 2024-05-15 2024-06-14 济南宝林信息技术有限公司 基于人工智能的心脏功能实时监听数据优化处理方法
CN118177830B (zh) * 2024-05-15 2024-08-09 济南宝林信息技术有限公司 基于人工智能的心脏功能实时监听数据优化处理方法

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