CN119924803A - Blood pressure prediction method, device and system - Google Patents
Blood pressure prediction method, device and system Download PDFInfo
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Abstract
The disclosure relates to a blood pressure prediction method, device and system, which comprise the steps of obtaining a user face picture and blood pressure related parameters of a user, wherein the blood pressure related parameters of the user are determined based on parameters collected by a PPG sensor, determining physiological characteristic information of the user according to the user face picture, wherein the physiological characteristic information of the user is at least one of age, gender, body mass index and skin color information, and determining a blood pressure predicted value of the user according to the physiological characteristic information of the user and the blood pressure related parameters of the user.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method, an apparatus, and a system for predicting blood pressure.
Background
Traditional blood pressure monitoring methods include arterial catheterization, auscultation, oscillography, etc. The arterial catheterization method can obtain continuous and accurate blood pressure values, but has larger damage to patients and is suitable for severe patients. The auscultation method and the oscillography method are two blood pressure monitoring methods commonly used at present, and belong to intermittent monitoring, and an inflatable cuff is needed, so that discomfort is brought to a user in the inflation and deflation process of the cuff.
Disclosure of Invention
An object of the present disclosure is to provide a new technical solution of a blood pressure prediction method.
According to a first aspect of the present disclosure, there is provided a blood pressure prediction method comprising:
Acquiring a user face picture and blood pressure related parameters of a user, wherein the blood pressure related parameters of the user are determined based on parameters acquired by a PPG sensor;
Determining physiological characteristic information of a user according to the face picture of the user, wherein the physiological characteristic information of the user is at least one of age, gender, body mass index and skin color information;
and determining a predicted blood pressure value of the user according to the physiological characteristic information of the user and the blood pressure related parameters of the user.
Optionally, the determining physiological characteristic information of the user according to the face picture of the user includes:
and inputting the face picture of the user into a set physiological characteristic recognition model of the user to obtain physiological characteristic information of the user.
Optionally, the set physiological characteristic recognition model of the user is determined based on training of a first training sample set and verification of a first test sample set, wherein each sample in the first training sample set and the first test sample set comprises a face picture and physiological characteristic information of the corresponding user.
Optionally, the determining the predicted value of the blood pressure of the user according to the physiological characteristic information of the user and the blood pressure related parameter of the user includes:
And inputting the physiological characteristic information of the user and the blood pressure related parameters of the user into a set blood pressure prediction model to obtain a blood pressure predicted value of the user.
Optionally, the set blood pressure prediction model is determined based on training of a second training sample set and testing of a second testing sample set, wherein each sample in the second training sample set and the second testing sample set comprises physiological characteristic information of a user, blood pressure related parameters of the user and corresponding blood pressure values.
Optionally, the method further comprises:
Obtaining a blood pressure measured value of a user, a weight coefficient corresponding to the blood pressure measured value and a weight coefficient corresponding to a blood pressure predicted value;
And determining a corrected blood pressure value of the user according to the predicted blood pressure value of the user, the blood pressure measured value of the user, the weight coefficient corresponding to the blood pressure measured value and the weight coefficient corresponding to the predicted blood pressure value.
Optionally, the obtaining the blood pressure measurement value of the user includes:
Acquiring a blood pressure measurement result picture, wherein the blood pressure measurement result picture shows a blood pressure measurement value;
and extracting the blood pressure measured value of the user from the blood pressure measured result picture.
Optionally, the blood pressure related parameter of the user is at least one of a time length of systole, a time length of diastole, a pulse interval, a time required for systole to reach a peak value, a time required for diastole to reach a peak value, a time interval between a peak value of systole and a peak value of diastole, a time interval between two adjacent peak values of systole, a systole width, and a diastole width.
According to a second aspect of the present disclosure, there is provided a blood pressure prediction apparatus including:
The acquisition module is used for acquiring the face picture of the user and the blood pressure related parameters of the user, wherein the blood pressure related parameters of the user are determined based on the parameters acquired by the PPG sensor;
The physiological characteristic information determining module is used for determining physiological characteristic information of a user according to the face picture of the user, wherein the physiological characteristic information of the user is at least one of age, gender, body mass index and skin color information;
and the blood pressure prediction module is used for determining a blood pressure predicted value of the user according to the physiological characteristic information of the user and the blood pressure related parameters of the user.
According to a third aspect of the present disclosure, there is provided a blood pressure prediction system comprising an electronic device for performing the method provided in the first aspect and a wearable device, wherein the wearable device establishes a communication connection with the electronic device, the wearable device being provided with a PPG sensor.
According to the blood pressure prediction method provided by the embodiment of the disclosure, the blood pressure predicted value of the user is obtained based on the physiological characteristic information of the user and the relevant parameters of the blood pressure of the user, so that the cuff-free blood pressure measurement of the blood pressure of the user is realized.
Features of the embodiments of the present specification and their advantages will become apparent from the following detailed description of exemplary embodiments of the present specification with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the embodiments of the specification.
Fig. 1 is a process flow diagram of a blood pressure prediction method according to one embodiment of the present disclosure.
Fig. 2 is a schematic graph of parameter determination based on PPG sensor acquisition according to one embodiment of the present disclosure.
FIG. 3 is a flow chart of determining physiological characteristic information of a user based on a set physiological characteristic recognition model of the user, according to one embodiment of the present disclosure.
Fig. 4 is a schematic diagram of fusion of physiological characteristic information of a user and blood pressure related parameters of the user according to one embodiment of the present disclosure.
Fig. 5 is a functional block diagram of a blood pressure prediction device according to one embodiment of the present disclosure.
Fig. 6 is a block diagram of a hardware architecture of an electronic device according to one embodiment of the present disclosure.
Fig. 7 is a block diagram of the device components of a blood pressure prediction system according to one embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the disclosure, its application, or uses.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Fig. 1 shows a flowchart of a blood pressure prediction method according to one embodiment of the present disclosure. The method can be applied to APP (Application). The APP can be a client APP or a webpage APP. As shown in fig. 1, the method includes steps S110 to S130.
Step S110, acquiring a user face picture and blood pressure related parameters of a user, wherein the blood pressure related parameters of the user are determined based on parameters acquired by a PPG sensor.
In some embodiments, in response to a user triggering a start APP, the APP interface displays an upload interaction control for a user's face picture. And after the user triggers the uploading interaction control, displaying a face picture window of the user. The user can upload the face picture of the user based on the face picture of the user. The user face picture can be selected from pictures locally stored by the terminal equipment provided with the APP and uploaded based on the user, and can be shot and uploaded based on the shooting module of the terminal equipment provided with the APP.
A PPG (Photoplethysmogram ) sensor is a biosensor that uses optical principles to monitor changes in vascular volume.
The terminal device provided with the APP is provided with a PPG sensor. Or the PPG sensor is provided on other terminal devices, which may be wearable devices, e.g. smart watches, smart bracelets.
Under the condition that the PPG sensor is arranged on other terminal equipment, the terminal equipment provided with the PPG sensor and the terminal equipment provided with the APP are in communication connection, and the communication connection can be any mode of WIFI connection, bluetooth connection and NFC connection. The terminal equipment provided with the PPG sensor sends parameters acquired by the PPG sensor to the terminal equipment provided with the APP through the communication connection. And the terminal equipment provided with the APP determines relevant parameters of blood pressure of the user according to the parameters acquired by the received PPG sensor. And the terminal equipment provided with the PPG sensor determines the blood pressure related parameters of the user according to the parameters acquired by the PPG sensor and sends the blood pressure related parameters of the user to the terminal equipment provided with the APP.
The blood pressure related parameter of the user is at least one of a length of systole, a length of diastole, a pulse interval, a time required for systole to peak, a time required for diastole to peak, a time interval between a systole peak and a diastole peak, a time interval between two adjacent systole peaks, a systole width, and a diastole width.
The duration of systole (T sys) is the time for which the heart muscle contracts and pumps blood into the systemic blood vessels.
The length of diastole (T dia) is the time that the heart relaxes and allows blood to flow from the atrium into the ventricle in preparation for the next contraction.
The pulse interval (T pi) is the time interval between two pulse beats.
The time required for the systole to peak (T sp) is the time required for the systole to peak in one cardiac cycle.
The time required for the diastole to peak (T dp) is the time required for the diastole to peak in one cardiac cycle.
The time interval (Δt) between the systolic peak and the diastolic peak is the time interval between the time at which the systole reaches the peak and the time at which the diastole reaches the peak in one cardiac cycle.
The time interval (T pp) between two adjacent systole peaks is the time interval between the times in the two adjacent cardiac cycles at which the systole peaks.
The cardiac cycle is the duration of each contraction and relaxation of the heart.
The systole width (T sw50) is the time required for the systole amplitude to increase from 50% of the peak-to-peak amplitude.
The diastolic width (T dw50) is the time required for the systolic amplitude to drop from the peak-to-peak amplitude to 50% of the peak-to-peak amplitude.
The curve shown in fig. 2 is a curve determined based on parameters acquired by the PPG sensor, and the meaning represented by the above-described blood pressure-related parameters of each user can be understood in conjunction with the curve.
Step S120, determining physiological characteristic information of the user according to the face picture of the user, wherein the physiological characteristic information of the user is at least one of age, gender, body mass index and skin color information.
Therefore, the operation of manually inputting the physiological characteristic information by the user can be omitted, errors caused by manually inputting the physiological characteristic information by the user are avoided, the interaction efficiency is improved, and the user experience is optimized.
The skin tone information is one of 6 types classified according to international-current Fitzpatrick Scale (feitzpatrick skin typing) standard.
In some embodiments, step S120 specifically includes inputting the user face picture into a set user physiological feature recognition model to obtain physiological feature information of the user.
The set user physiological characteristic recognition model is trained based on the first training sample set and determined based on the first test sample set. Each sample in the first training sample set and the first test sample set comprises a face picture and corresponding physiological characteristic information of a user. The physiological characteristic information of the user in each sample is artificially marked information.
The user physiological characteristic recognition model to be trained is a deep convolutional neural network. The deep neural network comprises an input layer, a convolution layer, a full connection layer and an output layer. Firstly, training a physiological characteristic recognition model of a user to be trained by using a first training sample set, so that the physiological characteristic recognition model of the user to be trained learns the relevance between face pictures in each training sample and physiological characteristic information of a corresponding user. And then, verifying the trained user physiological characteristic recognition model by using the first test sample set, stopping training the user physiological characteristic recognition model under the condition that the output result of the user physiological characteristic recognition model meets the preset requirement, and taking the trained user physiological characteristic recognition model as a set user physiological characteristic recognition model.
Fig. 3 shows a schematic diagram of obtaining physiological characteristic information of a user based on a face picture by using a set physiological characteristic recognition model of the user. Referring to fig. 3, the input layer is configured to determine a multidimensional array based on the input face picture, where the multidimensional array retains key semantic information after being processed by the multi-layer convolution layer, and then outputs corresponding physiological characteristic information of the user through the full connection layer.
In some embodiments, after inputting the user face picture to the set user physiological feature recognition model, preprocessing the user face picture to obtain a preprocessed user face picture, and inputting the preprocessed user face picture to the set user physiological feature recognition model. The preprocessing eight comprises picture size adjustment, filtering noise reduction, normalization processing and face picture part clipping from the face picture of the user.
Step S130, determining a predicted value of the blood pressure of the user according to the physiological characteristic information of the user and the blood pressure related parameters of the user.
The blood pressure predictors include a systolic pressure predictor and a diastolic pressure predictor.
In some embodiments, the step S130 specifically includes inputting the physiological characteristic information of the user and the blood pressure related parameters of the user into a set blood pressure prediction model to obtain a predicted blood pressure value of the user.
According to fig. 4, the physiological characteristic information of the user is an array, the blood pressure related parameter of the user is an array, and before the physiological characteristic information of the user and the blood pressure related parameter of the user are input into the set blood pressure prediction model, the two arrays are fused to obtain a fused array, and the fused array is used as a vector and is input into the set blood pressure prediction model.
The set user blood pressure prediction model is determined based on the second training sample set training and the second test sample set. Each sample in the second training sample set and the second test sample set comprises physiological characteristic information of the user, blood pressure related parameters of the user and corresponding blood pressure values. The blood pressure value in each sample is an actual measurement of the blood pressure of the user.
The user blood pressure prediction model to be trained is a machine learning model (e.g., logistic regression, support vector machine) or a deep learning model (e.g., convolutional neural network, recurrent neural network). Firstly, training a user blood pressure prediction model to be trained by using a second training sample set, so that the user blood pressure prediction model to be trained learns the relevance of physiological characteristic information of a user, blood pressure related parameters of the user and corresponding blood pressure values in each training sample. And then, verifying the trained user blood pressure prediction model by using the second test sample set, stopping training the user blood pressure prediction model under the condition that the output result of the user blood pressure prediction model meets the preset requirement, and taking the trained user blood pressure prediction model as a set user blood pressure prediction model.
According to the blood pressure prediction method provided by the embodiment of the disclosure, the blood pressure predicted value of the user is obtained based on the physiological characteristic information of the user and the relevant parameters of the blood pressure of the user, so that the cuff-free blood pressure measurement of the blood pressure of the user is realized.
In some embodiments, the method further comprises obtaining a blood pressure measurement value of the user, a weight coefficient corresponding to the blood pressure measurement value, and a weight coefficient corresponding to the blood pressure prediction value, and determining a corrected blood pressure value of the user based on the blood pressure prediction value of the user, the blood pressure measurement value of the user, the weight coefficient corresponding to the blood pressure measurement value, and the weight coefficient corresponding to the blood pressure prediction value. The predicted blood pressure value of the user can be corrected through the blood pressure measured value of the user, and the accuracy of the blood pressure value result of the user is ensured.
The blood pressure measurements include systolic and diastolic blood pressure measurements.
Based on the following calculation formula, a corrected blood pressure value BP of the user is determined,
BP=BP1×α+BP2×β
Wherein BP 1 is a predicted blood pressure value, alpha is a weight coefficient corresponding to the predicted blood pressure value, BP 2 is a measured blood pressure value, and beta is a weight coefficient corresponding to the measured blood pressure value. When the predicted blood pressure value of BP 1 is the predicted systolic blood pressure value, BP 2 is the systolic blood pressure measurement. When the predicted blood pressure value of BP 1 is the predicted diastolic blood pressure value, BP 2 is the diastolic blood pressure measurement.
The blood pressure measurement of the user is a historical blood pressure measurement and is not the current blood pressure measurement of the user.
The blood pressure measured value of the user is the blood pressure measured value which is triggered and uploaded by the user on the APP interface. Or the blood pressure measured value of the user is determined based on the blood pressure measured result picture which is triggered and uploaded by the user on the APP interface.
The method for determining the blood pressure measurement value of the user based on the blood pressure measurement result picture comprises the steps of obtaining the blood pressure measurement result picture, wherein the blood pressure measurement result picture shows the blood pressure measurement value, and extracting the blood pressure measurement value of the user from the blood pressure measurement result picture. First, each character including kanji, english, and numerals is recognized from the blood pressure measurement result picture by OCR (Optical Character Recognition ). Then, using NER (NAMED ENTITY Recognition ), a blood pressure measurement is determined from the characters obtained by the Recognition. Therefore, the automatic identification of the blood pressure measured value of the user can be realized, the operation of manually inputting the blood pressure measured value by the user is omitted, the interaction efficiency is improved, and the user experience is optimized.
The present embodiment provides a blood pressure prediction device for implementing any of the above method embodiments. Fig. 5 shows a block diagram of a blood pressure prediction device according to one embodiment of the present disclosure. As shown in fig. 5, the blood pressure predicting apparatus 500 includes an acquisition module 510, a physiological characteristic information determining module 520, and a blood pressure predicting module 530.
The acquisition module 510 is configured to acquire a face image of a user and a blood pressure related parameter of the user, where the blood pressure related parameter of the user is determined based on a parameter acquired by the PPG sensor;
the physiological characteristic information determining module 520 is configured to determine physiological characteristic information of a user according to a face picture of the user, where the physiological characteristic information of the user is at least one of age, gender, body mass index and skin color information.
The blood pressure prediction module 530 is configured to determine a predicted blood pressure value of the user according to the physiological characteristic information of the user and the blood pressure related parameter of the user.
In some embodiments, the set user physiological characteristic recognition model is determined based on a first training sample set training and a first test sample set verification, wherein each sample in the first training sample set and the first test sample set comprises a face picture and corresponding physiological characteristic information of the user.
In some embodiments, the blood pressure prediction module 530 is further configured to input physiological characteristic information of the user and blood pressure related parameters of the user into a set blood pressure prediction model to obtain a predicted blood pressure value of the user.
In some embodiments, the set blood pressure prediction model is determined based on a second training sample set training and a second test sample set testing, wherein each sample in the second training sample set and the second test sample set comprises physiological characteristic information of the user, blood pressure related parameters of the user, and corresponding blood pressure values.
In some embodiments, the apparatus further comprises a correction module. The correction module is used for obtaining the blood pressure measured value of the user, the weight coefficient corresponding to the blood pressure measured value and the weight coefficient corresponding to the blood pressure predicted value, and determining the corrected blood pressure value of the user according to the blood pressure predicted value of the user, the blood pressure measured value of the user, the weight coefficient corresponding to the blood pressure measured value and the weight coefficient corresponding to the blood pressure predicted value.
In some embodiments, the correction module is used for obtaining a blood pressure measurement picture, wherein the blood pressure measurement picture displays blood pressure measurement values, and extracting the blood pressure measurement values of the user from the blood pressure measurement picture.
The present disclosure also provides an electronic device for implementing any of the above method embodiments. Fig. 6 shows a block diagram of a hardware architecture of an electronic device 600 according to one embodiment of the disclosure. As shown in fig. 6, the electronic device 600 includes a processor 610 and a memory 620 for storing instructions executable by the processor 610. The processor 610 is configured to implement methods according to any embodiment of the present disclosure when executing instructions stored by the memory 620.
The processor 610 is configured to execute computer instructions that may be written in an instruction set of an architecture such as x86, arm, RISC, MIPS, SSE, etc. The memory 620 includes, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, and the like, and is not limited thereto.
The present disclosure also provides a non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method provided by any of the embodiments described above.
The present disclosure also provides a blood pressure prediction system for implementing any of the above method embodiments. Fig. 7 shows a schematic diagram of the device composition of a blood pressure prediction system according to one embodiment of the present disclosure.
According to fig. 7, the system comprises an electronic device and a wearable device. The wearable device establishes communication connection with the electronic device, and the wearable device is provided with a PPG sensor.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. For the device embodiments, reference is made to the description of parts of the method embodiments for its relevance.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Embodiments of the present description may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer instructions for causing a processor to implement aspects of embodiments of the present description.
The computer readable storage medium may be a tangible device that can hold and store computer instructions for use by a computer instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, punch cards or intra-groove protrusion structures such as those having computer instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device via a network layer, such as the internet, a local area network, a wide area network, and/or a wireless network. The network layer may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network layer adapter card or network layer interface in each computing/processing device receives computer instructions from the network layer and forwards the computer instructions for storage in a computer readable storage medium in the respective computing/processing device.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present description. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of computer instructions, which comprises one or more executable computer instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The embodiments of the present specification have been described above, and the above description is illustrative, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
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| CN114818910A (en) * | 2022-04-22 | 2022-07-29 | 北京邮电大学 | A non-contact blood pressure detection model training method, blood pressure detection method and device |
| CN116127322A (en) * | 2023-02-10 | 2023-05-16 | 北京安芯测科技有限公司 | Training method and device of personalized blood pressure prediction model and electronic equipment |
| CN117426759A (en) * | 2023-10-13 | 2024-01-23 | 深圳市奋达智能技术有限公司 | Cuffless blood pressure measurement method, cuffless blood pressure calibration method and related equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114818910A (en) * | 2022-04-22 | 2022-07-29 | 北京邮电大学 | A non-contact blood pressure detection model training method, blood pressure detection method and device |
| CN116127322A (en) * | 2023-02-10 | 2023-05-16 | 北京安芯测科技有限公司 | Training method and device of personalized blood pressure prediction model and electronic equipment |
| CN117426759A (en) * | 2023-10-13 | 2024-01-23 | 深圳市奋达智能技术有限公司 | Cuffless blood pressure measurement method, cuffless blood pressure calibration method and related equipment |
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