Heart-sound segmentation method and apparatus, and electronic device and readable storage medium

Abstract

The present application relates to the technical field of medical signal processing. Disclosed are a heart-sound segmentation method and apparatus, and an electronic device and a readable storage medium. The heart-sound segmentation method comprises: synchronously collecting physiological signals, wherein the physiological signals comprise a heart sound signal; acquiring a segmentation threshold value corresponding to the heart sound signal, and segmenting the heart sound signal on the basis of the segmentation threshold value, so as to obtain a first heart sound and a second heart sound; and on the basis of the first heart sound and the second heart sound, determining the systole duration of the heart sound signal and the diastole duration of the heart sound signal. The present application reduces the consumption of resources for heart sound segmentation of heart sound signals.

Description

Heart sound segmentation method and device, electronic equipment and readable storage medium TECHNICAL FIELD

[0001] The present application relates to the technical field of medical signal processing, in particular to a heart sound segmentation method and device, electronic equipment and readable storage medium. BACKGROUND

[0002] A key part of computer-aided heart sound analysis is the segmentation of heart sound signals, specifically distinguishing the exact location of the first heart sound (S1), the systolic period, the second heart sound (S2) and the diastolic period in each cardiac cycle. In each cardiac cycle, the first heart sound (S1) is caused by the closing of the mitral and tricuspid valves, the opening of the aortic and pulmonary valves, and the vibration caused by the contraction of the ventricle; the second heart sound (S2) is mainly caused by the closing of the aortic and pulmonary valves, and the vibration caused by the weakening of the aortic and pulmonary blood flow. The systolic period is the interval between S1 and S2, and the diastolic period is from S2 to the beginning of the next cardiac cycle S1. The correctness of heart sound signal segmentation directly affects the analysis effect of subsequent heart sound signals.

[0003] In related technologies, network models are usually used for heart sound segmentation. The segmentation algorithm based on the logistic regression hidden semi-Markov model (LR-HSMM) is one of the commonly used heart sound segmentation methods. However, the model-based heart sound segmentation method requires complex model calculations, which has high computational complexity and high resource consumption. Therefore, there is an urgent need for a heart sound segmentation method for heart sound signals with low resource consumption.

[0004] The disclosure of the above background art content is only used to assist in understanding the inventive concept and technical solutions of the present application, and does not necessarily belong to the prior art of the present patent application, nor does it necessarily provide technical teaching, in order to provide general background information, and does not necessarily constitute prior art. SUMMARY

[0005] The main purpose of the present application is to provide a heart sound segmentation method, device, electronic equipment and readable storage medium, which aims to solve the technical problem of how to reduce the resource consumption of heart sound segmentation of heart sound signals on a server.

[0006] To achieve the above-mentioned purpose, the present application provides a heart sound segmentation method, which comprises:

[0007] synchronously collecting physiological signals, wherein the physiological signals include heart sound signals;

[0008] obtaining a segmentation threshold corresponding to the heart sound signal, segmenting the heart sound signal based on the segmentation threshold to obtain a first heart sound and a second heart sound;

[0009] determining a systolic duration of the heart sound signal and a diastolic duration of the heart sound signal according to the first heart sound and the second heart sound.

[0010] Optionally, the physiological signal further comprises an electrocardiogram signal and a pulse signal, and before the step of obtaining the segmentation threshold corresponding to the heart sound signal, the method further comprises:

[0011] determining a QRS peak point of the electrocardiogram signal, and taking a time corresponding to the QRS peak point as a first time;

[0012] determining a trough point of the pulse signal, and taking a time corresponding to the trough point as a second time;

[0013] determining a first zero-crossing point and a second zero-crossing point of the pulse signal, taking a time corresponding to the first zero-crossing point as a third time, and taking a time corresponding to the second zero-crossing point as a fourth time;

[0014] determining a first threshold corresponding to the heart sound signal at the first time and the second time, and determining a second threshold corresponding to the heart sound signal at the third time and the fourth time;

[0015] taking the first threshold and the second threshold as the segmentation threshold.

[0016] Optionally, the step of determining the first threshold corresponding to the first time and the second time comprises:

[0017] obtaining or determining a heart sound envelope signal corresponding to the heart sound signal;

[0018] taking a heart sound peak value of the heart sound envelope signal between the first time and the second time as a first heart sound peak value;

[0019] adjusting the first heart sound peak value based on a preset adjustment coefficient to obtain the first threshold, wherein the first threshold is less than the first heart sound peak value.

[0020] Optionally, the step of determining the second threshold corresponding to the third time and the fourth time comprises:

[0021] obtaining or determining a heart sound envelope signal corresponding to the heart sound signal;

[0022] taking a heart sound peak value of the heart sound envelope signal between the third time and the fourth time as a second heart sound peak value;

[0023] adjust the second heart sound peak based on a preset adjustment coefficient to obtain a second threshold, wherein the second threshold is less than the second heart sound peak.

[0024] Optionally, the segmentation threshold includes a first threshold and a second threshold, and the step of segmenting the heart sound signal based on the segmentation threshold includes:

[0025] obtaining or determining a heart sound envelope signal corresponding to the heart sound signal;

[0026] taking a duration window in which the signal value of the heart sound envelope signal is greater than the first threshold as a first duration window;

[0027] taking a duration window in which the signal value of the heart sound envelope signal is greater than the second threshold as a second duration window;

[0028] taking the heart sound signal in the first duration window as a first heart sound and taking the heart sound signal in the second duration window as a second heart sound.

[0029] Optionally, the step of determining the systolic duration and diastolic duration of the heart sound signal according to the first heart sound and the second heart sound includes:

[0030] determining a heart cycle included in the heart sound signal, and sequentially traversing each of the heart cycles;

[0031] taking the end time of the first heart sound in the traversed heart cycle as a first end time, determining a next heart cycle corresponding to the traversed heart cycle, and taking the start time of the first heart sound in the next heart cycle as a first start time;

[0032] taking the start time of the second heart sound in the traversed heart cycle as a second start time, and taking the end time of the second heart sound in the traversed heart cycle as a second end time;

[0033] taking the time difference between the first end time and the second start time as a systolic duration;

[0034] taking the time difference between the first start time and the second end time as a diastolic duration;

[0035] until the traversal of each of the heart cycles is completed, determining an average systolic duration of all the systolic durations and an average diastolic duration of all the diastolic durations;

[0036] taking the average systolic duration as the systolic duration of the heart sound signal and taking the average diastolic duration as the diastolic duration of the heart sound signal.

[0037] Further, in order to achieve the above object, the present application also provides a heart sound segmentation device, comprising:

[0038] a signal acquisition module, configured to synchronously acquire a physiological signal, wherein the physiological signal comprises a heart sound signal;

[0039] a segmentation module, configured to acquire a segmentation threshold corresponding to the heart sound signal, segment the heart sound signal based on the segmentation threshold, and obtain a first heart sound and a second heart sound;

[0040] a determination module, configured to determine a systolic duration and a diastolic duration according to the first heart sound and the second heart sound.

[0041] The present application also provides an electronic device, which is a physical device, comprising at least one processor, and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the heart sound segmentation method.

[0042] The present application also provides a readable storage medium, which is a computer readable storage medium, and the computer readable storage medium stores a program for implementing a heart sound segmentation method, and the program is executed by a processor to implement the steps of the heart sound segmentation method.

[0043] The present application also provides a computer program product, comprising a computer program, which is executed by a processor to implement the steps of the heart sound segmentation method.

[0044] In the present application, a physiological signal is synchronously acquired, wherein the physiological signal comprises a heart sound signal; a segmentation threshold corresponding to the heart sound signal is acquired, the heart sound signal is segmented based on the segmentation threshold, and a first heart sound and a second heart sound are obtained; and a systolic duration and a diastolic duration are determined according to the first heart sound and the second heart sound. Thus, compared with the model-based heart sound segmentation method of the prior art, the present application performs heart sound segmentation on the heart sound signal based on a segmentation threshold to obtain a first heart sound and a second heart sound, calculates the diastolic period and the systolic period based on the first heart sound and the second heart sound, completes complete heart sound segmentation of the heart sound signal, and only needs simple value comparison operation to complete heart sound segmentation, without complex model operation, thereby reducing the calculation complexity and the resource consumption of heart sound segmentation of the heart sound signal. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application together with the specification.

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those of ordinary skill in the art, the other drawings can be obtained based on these drawings without any creative effort.

[0047] Fig. 1 is a flowchart of a first embodiment of the heart sound segmentation method of the present application;

[0048] Fig. 2 is a schematic diagram of the overall architecture of the wearable device system of the present application;

[0049] Fig. 3 is a schematic diagram of the structure of the wearable device of the present application;

[0050] Fig. 4 is a schematic diagram of the physiological signal of the heart sound segmentation method of the present application;

[0051] Fig. 5 is a schematic diagram of the prediction flow of the heart sound segmentation method of the present application;

[0052] Fig. 6 is another schematic diagram of the prediction flow of the heart sound segmentation method of the present application;

[0053] Fig. 7 is a schematic diagram of the device module of the heart sound segmentation device of the present application;

[0054] Fig. 8 is a schematic diagram of the device structure of the hardware operating environment involved in the heart sound segmentation device of the embodiment of the present application.

[0055] The purposes, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort fall within the scope of protection of the present application.

[0057] Embodiment One

[0058] The present application proposes a heart sound segmentation method of the first embodiment, which will be described with reference to Fig. 1. The heart sound segmentation method comprises:

[0059] Step S10, synchronously collecting physiological signals, wherein the physiological signals include heart sound signals;

[0060] The physiological signal includes but is not limited to heart sound signal, and can also include electrocardiogram signal and pulse signal, so as to assist heart sound segmentation of the heart sound signal and improve segmentation accuracy. The physiological signal can be collected by a related device. For example, if a user uses a wearable device, the physiological signal of the user can be collected based on the wearable device. The wearable device can be a smart watch, a smart bracelet or the like.

[0061] Further, the physiological signals are simultaneously started and synchronously collected at the same time, the relationship between the electrocardiogram signal, the pulse signal and the heart sound signal and time is determined, so as to ensure strict synchronization of the physiological signals. The time when the data is recorded from the start of collection can also be recorded based on the real-time time after correction.

[0062] In step S20, a segmentation threshold corresponding to the heart sound signal is obtained, and the heart sound signal is segmented based on the segmentation threshold to obtain a first heart sound and a second heart sound.

[0063] The heart sound segmentation of the heart sound signal specifically distinguishes the exact positions of the first heart sound (S1), the systolic period, the second heart sound (S2) and the diastolic period in each cardiac cycle. The first heart sound (S1) is caused by the closing of the mitral valve and the tricuspid valve, the opening of the aortic valve and the pulmonary valve and the vibration caused by the contraction of the ventricle. The second heart sound (S2) is mainly caused by the closing of the aortic valve and the pulmonary valve, and the vibration caused by the weakening of the blood flow in the aorta and the pulmonary artery. The systolic period is the interval between S1 and S2, and the diastolic period is from S2 to the beginning of the next cardiac cycle S1. The correctness of the heart sound signal segmentation directly affects the analysis effect of the subsequent heart sound signal. The systolic period duration is the duration between S1 and S2, and the diastolic period duration is the duration between S2 and the next cardiac cycle S1.

[0064] In an available implementation, the segmentation threshold includes a first threshold and a second threshold, and the step of segmenting the heart sound signal based on the segmentation threshold includes:

[0065] In step S201, a heart sound envelope signal corresponding to the heart sound signal is obtained or determined.

[0066] Specifically, an upper envelope line of the heart sound signal can be determined, and the heart sound envelope signal corresponding to the heart sound signal is obtained by filtering the upper envelope line.

[0067] In step S202, a duration window in which the signal value of the heart sound envelope signal is greater than the first threshold is taken as a first duration window.

[0068] In step S203, a duration window in which the signal value of the heart sound envelope signal is greater than the second threshold is taken as a second duration window.

[0069] Step S204, the heart sound signal within the first duration window is taken as a first heart sound, and the heart sound signal within the second duration window is taken as a second heart sound.

[0070] It should be noted that the filtering processing on the heart sound signal can be baseline removal and moving average filtering processing, so as to reduce the interference signal.

[0071] It can be understood that for each heart cycle, there is a corresponding first duration window and a second duration window, and the first heart sound within the first duration window is denoted as S1(t), where t is the tth heart cycle, and the second heart sound within the second duration window is denoted as S2(t). Referring to FIG. 4, the physiological signal includes an electrocardiogram (ECG) signal, a phonocardiogram (PCG) signal, and a pulse signal. Exemplarily, the heart sound signal is subjected to heart sound segmentation, and in each heart cycle, the first heart sound (S1) and the second heart sound (S2) are segmented.

[0072] Step S30, determining a systolic duration of the heart sound signal and a diastolic duration of the heart sound signal according to the first heart sound and the second heart sound.

[0073] The first heart sound is denoted as S1, the second heart sound is denoted as S2, the systole is the interval between S1 and S2, and the diastole is from S2 to the beginning of the next heart cycle S1. That is, the systolic duration is the interval between S1 and S2, and the diastolic duration is the duration from S2 to the beginning of the next heart cycle S1.

[0074] In a possible implementation, the step of determining the systolic duration of the heart sound signal and the diastolic duration of the heart sound signal according to the first heart sound and the second heart sound includes:

[0075] Step S301, determining the heart cycles included in the heart sound signal, and sequentially traversing each of the heart cycles;

[0076] Step S302, taking the end time of the first heart sound in the traversed heart cycle as a first end time, determining the next heart cycle corresponding to the traversed heart cycle, and taking the start time of the first heart sound in the next heart cycle as a first start time;

[0077] Step S303, taking the start time of the second heart sound in the traversed heart cycle as a second start time, and taking the end time of the second heart sound in the traversed heart cycle as a second end time;

[0078] Step S304, taking the time difference between the first end time and the second start time as the contraction period duration;

[0079] Step S305, taking the time difference between the first start time and the second end time as the duration of the diastolic period;

[0080] Step S306, after each cardiac cycle is traversed, determining the average systolic duration of all the systolic durations, and determining the average diastolic duration of all the diastolic durations;

[0081] Step S307 : Using the average systolic duration as the systolic duration of the heart sound signal, and using the average diastolic duration as the diastolic duration of the heart sound signal.

[0082] The first heart sound of the t-th cardiac cycle is recorded as S1(t), the second heart sound of the t-th cardiac cycle is recorded as S2(t), and the starting time of S1(t) is recorded as S1 start (t), the end time of S1(t) is recorded as S1 end (t), the starting time of S2(t) is recorded as S2 start (t), the end time of S2(t) is recorded as S2 end (t), then the duration of the systolic phase corresponding to the t-th cardiac cycle is S1 end (t) and S2 start (t) The time difference between the two, the duration of diastole is S1 start (t+1) and S2 end (t), based on this, if the heart sound signal includes the heart sound signals of multiple cardiac cycles, then for each cardiac cycle corresponding to a systolic duration and a diastolic duration, the average duration of the systolic duration corresponding to all cardiac cycles can be used as the final systolic duration, and similarly, the average duration of the diastolic duration corresponding to all cardiac cycles can be used as the final diastolic duration.

[0083] Example 2

[0084] Based on the first embodiment of the present application, in another embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated hereafter. On this basis, in a feasible implementation, the physiological signal also includes an electrocardiogram signal and a pulse signal, and before the step of obtaining the segmentation threshold corresponding to the heart sound signal, the method further includes:

[0085] Step A10, determining the QRS wave peak point of the electrocardiogram signal, and taking the time corresponding to the QRS wave peak point as the first time;

[0086] Step A20, determining a trough point of the pulse signal, taking the time corresponding to the trough point as a second time;

[0087] Step A30, determining a first zero-crossing point and a second zero-crossing point of the pulse signal, taking the time corresponding to the first zero-crossing point as a third time, and taking the time corresponding to the second zero-crossing point as a fourth time;

[0088] Step A40, determining a first threshold value corresponding to the first time and the second time, and determining a second threshold value corresponding to the third time and the fourth time;

[0089] Step A50, taking the first threshold value and the second threshold value as a segmentation threshold value.

[0090] It can be understood that, for the electrocardiogram signal and the pulse signal of each cardiac cycle, the first time corresponding to the QRS peak point of the electrocardiogram signal in the cardiac cycle, the second time corresponding to the trough point of the pulse signal, and the third time and the fourth time of the first zero-crossing point and the second zero-crossing point of the pulse signal are determined, and then the first threshold value and the second threshold value of the cardiac cycle are determined. The heart sound segmentation of the heart sound signal of the cardiac cycle is performed based on the first threshold value and the second threshold value of the cardiac cycle, the second systolic duration and the second diastolic duration of the heart sound signal of the cardiac cycle are obtained, so that the heart sound segmentation of the heart sound signal is improved.

[0091] In an available implementation, the step of determining the first threshold value corresponding to the first time and the second time comprises:

[0092] Step B10, obtaining or determining a heart sound envelope signal corresponding to the heart sound signal;

[0093] Step B20, taking a heart sound peak value of the heart sound envelope signal between the first time and the second time as a first heart sound peak value;

[0094] Step B30, adjusting the first heart sound peak value based on a preset adjustment coefficient to obtain a first threshold value, wherein the first threshold value is less than the first heart sound peak value.

[0095] The preset adjustment coefficient can be any coefficient set in advance, such as 0.1, 0.15, 0.2, etc. The first threshold value can be obtained by multiplying the first heart sound peak value by the preset adjustment coefficient.

[0096] In the embodiment, the first threshold value is determined according to the first heart sound peak value between the first time and the second time, the first threshold value is related to the first heart sound peak value, different peak values correspond to different threshold values, so that intelligent adjustment of the threshold value can be realized, instead of heart sound segmentation with a fixed threshold value, and the segmentation accuracy of the heart sound segmentation is improved.

[0097] In a possible implementation, the step of determining the second threshold value corresponding to the third time and the fourth time comprises:

[0098] Step C10, acquiring or determining a heart sound envelope signal corresponding to the heart sound signal;

[0099] Step C20, taking a heart sound peak value of the heart sound envelope signal between the third time and the fourth time as a second heart sound peak value;

[0100] Step C30, adjusting the second heart sound peak value based on a preset adjustment coefficient to obtain a second threshold value, wherein the second threshold value is less than the second heart sound peak value.

[0101] The preset adjustment coefficient can be the same or different from the above-mentioned adjustment coefficient, and the second threshold value can also be obtained by multiplying the second heart sound peak value by the preset adjustment coefficient. The second threshold value is related to the second peak value, different peak values correspond to different threshold values, so that intelligent adjustment of the threshold value can be realized, instead of heart sound segmentation with a fixed threshold value, and the segmentation accuracy of the heart sound segmentation is improved.

[0102] In order to help understand the technical concept or technical principle of the present application, a specific embodiment is listed:

[0103] In the specific embodiment, the heart sound segmentation process is as follows:

[0104] S1, identifying a QRS wave peak point of an electrocardiogram signal to obtain a peak point position sequence R(t);

[0105] S2, identifying a pulse signal trough point and two zero-crossing points to obtain a trough point position sequence V(t), a first zero-crossing point position sequence Cross_zero1(t), and a second zero-crossing point position sequence Cross_zero2(t);

[0106] S3, calculating an upper envelope line of the heart sound signal, performing baseline elimination and sliding average filtering on the upper envelope line to eliminate baseline drift and noise interference;

[0107] S4, calculating a peak value of the heart sound envelope signal between the R(t) and V(t) sequences, setting a threshold value to be 10% of the peak value, and taking a starting point sequence S1 start (t) and an ending point sequence S1 end(t) the time difference as S1 duration sequence S1(t);

[0108] S5, calculate the peak value of the heart sound envelope signal between Cross_zero1(t) and Cross_zero2(t) sequence, the threshold size is also 10% of the peak value, the envelope starting point sequence S2 start (t) and ending point sequence S2 end (t) the time difference as S2 duration sequence S2(t);

[0109] S6, using S1 end (t) and S2 start (t) time difference to calculate the systolic duration sequence Sys(t), using S1 star t(t+1) and S2 end (t) diastolic duration sequence Dia(t).

[0110] It should be noted that the above specific embodiments are only for understanding the present application and do not constitute a limitation on the heart sound segmentation process of the present application. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0111] Example three

[0112] Blood pressure is an important physiological parameter reflecting human health. With the acceleration of population aging and the change of modern people's lifestyle, the incidence of hypertension is also rising. As of 2022, the number of people with hypertension in China reached 245 million, while the control rate and awareness rate of hypertension were very low, only 56.6% and 16.8%. Hypertension is one of the important risk factors for cardiovascular diseases, and cardiovascular diseases are the "first killer" of human health. Therefore, monitoring blood pressure through a precise and convenient device can greatly help the prevention of hypertension.

[0113] The traditional blood pressure measurement method is mostly based on the oscillographic method of the cuff type electronic sphygmomanometer. However, this method causes great discomfort to the user during the measurement process due to the inflation and deflation of the cuff, and the measurement device is large in size and not portable. In recent years, the emergence of sleeveless blood pressure measurement devices has solved the discomfort of the cuff and greatly reduced the size. With the gradual increase in the number of users of wearable devices in recent years, people's demand for measuring blood pressure with wearable devices has also increased, especially some sleeveless measurement devices are integrated into smart watches, improving user popularity and measurement real-time, but the accuracy of current sleeveless blood pressure measurement devices needs to be improved.

[0114] The cuffless blood pressure measurement device estimates blood pressure mainly through pulse wave transit time. Pulse wave transit time (PWTT) is defined as the time required for the cardiac ejection to reach the distal point from the proximal point at the same time. The pulse wave transit time is usually obtained by synchronously collecting the electrocardiogram signal and the pulse wave signal, taking the R wave peak of the electrocardiogram signal as the starting point, and taking the pulse wave feature point as the end point. The time difference between the two points is the pulse wave transit time.

[0115] However, the R wave peak is not the time when the heart starts to contract. There is a preparation time before the heart starts to contract, which is called the preejection period (PEP). Due to the existence of the preejection period, the blood pressure result estimated based on the pulse wave transit time is unreliable.

[0116] Based on the above problems, and the first embodiment and the second embodiment of the present application, in another embodiment of the present application, the same or similar contents as the above embodiment one or embodiment two or embodiment three can be referred to the above introduction, and will not be described in detail. On this basis, in a feasible implementation manner, after the step of determining the systolic duration of the heart sound signal and the diastolic duration of the heart sound signal according to the first heart sound and the second heart sound, the method further comprises:

[0117] Step D10, extracting features from the physiological signal to obtain signal features, wherein the signal features include the preejection period duration and the pulse wave transit time;

[0118] In this embodiment, the heart sound segmentation method is applied to a wearable device, which can be a smart watch, a smart bracelet, etc.

[0119] For example, referring to FIGS. 2-3, the wearable device includes a housing 1 and a sensor disposed on the housing, the sensor being configured to synchronously collect physiological signals. The sensor includes a first sensor for collecting heart sound signals, a second sensor for collecting pulse signals, and a third sensor for collecting electrocardiogram signals. The first sensor can be a VPU (Voice Pick Up) sensor 250, the second sensor can be an optical pulse sensor 240, and the third sensor can be an electrode sensor. Specifically, the electrode sensor can include three electrodes, a first electrode 210 and a third electrode 230 for forming a loop for electrocardiogram signal collection, and a second electrode 220 for providing a reference point to eliminate the potential difference between the body and the wearable device and improve the signal-to-noise ratio of the electrocardiogram signal collection.

[0120] Further, referring to FIGS. 2-3, the wearable device can further include a device switch 110 for switching the wearable device on and off; a device processor 120 for executing program codes in the memory to perform various functions of the wearable device; a time calibration module 130 for displaying real-time time and data synchronization acquisition calibration; an interaction module 140 for collecting personalized information of a user, signals generated in response to the user performing a blood pressure measurement operation, and simple blood pressure measurement use instructions, the signals generated in response to the user performing the blood pressure operation including a blood pressure measurement start signal, and the use instructions including a blood pressure measurement signal acquisition position and a user acquisition posture; a physiological signal acquisition module 150 for collecting physiological signals related to blood pressure measurement of a user and acceleration and gyroscope signals. The physiological signals related to blood pressure measurement include electrocardiogram signals, pulse signals, and heart sound signals. A data processing module 160 for processing physiological signal segments collected by the physiological signal measurement module in real time, the data processing steps mainly including signal denoising, signal quality assessment, and feature extraction. A wireless communication module 180 for transmitting physiological signal data, real user blood pressure values, and user personalized information collected by the wearable device to a server or terminal through a wireless module for construction of a blood pressure measurement database. A blood pressure measurement module 170 for analyzing physiological signal data and / or personalized information of a user, ultimately performing blood pressure prediction, returning a blood pressure prediction result, and determining a current blood pressure level.

[0121] In addition, referring to FIG. 3, the physiological signal acquisition module further includes a 6-axis signal acquisition module, and acceleration and gyroscope signals are mainly acquired through the 6-axis signal acquisition module. The 6-axis signal acquisition module is mainly composed of a 6-axis sensor 260 integrated inside the device. Before blood pressure measurement is performed, the 6-axis signal acquisition module can calculate current Euler angles to assist a user in positioning an acquisition position and a posture. During blood pressure measurement, the 6-axis signal acquisition module can monitor movement of a user's arm, combine with electrocardiogram signals, heart sound signals, and pulse signals to perform physiological signal denoising, and can improve signal-to-noise ratios of the signals during acquisition.

[0122] It can be understood that the structure shown in the embodiments of the present application does not constitute a specific limitation on the wearable device. It can have more or fewer components described above, or combine certain components, or split certain components, or different component arrangements. The various components described above can be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing or application-specific integrated circuits.

[0123] The detection object can be a user using the wearable device. Further, the physiological signals are simultaneously and synchronously collected, the relationship between the electrocardiogram signal, the pulse signal, the heart sound signal and time is determined to ensure strict synchronization of the physiological signals. The data can be recorded from the time when the collection is started through the real-time time after the correction.

[0124] Further, before extracting the signal features of the physiological signals, the physiological signals can be processed, such as filtering, noise reduction, segmentation and the like, to improve the accuracy of the extracted signal features. The signal features are extracted based on the processed physiological signals.

[0125] In an available embodiment, the step of extracting the features of the physiological signals comprises:

[0126] In step D101, the physiological signals are pre-processed to obtain pre-processed physiological signals, wherein the signal pre-processing comprises one or more of filtering, normalization and noise reduction.

[0127] In this embodiment, preferably, the signal pre-processing comprises normalization, filtering and noise reduction. Optionally, the collected physiological signals are normalized, and the electrocardiogram signal, the pulse signal and the heart sound signal are band-pass filtered through a pre-designed FIR band-pass filter. The filtered data and the acceleration signal are further processed through adaptive filtering to filter out noise caused by motion artifacts, to obtain clean and high-quality physiological signals.

[0128] In step D102, the signal duration of the pre-processed physiological signals is determined.

[0129] In step D103, if the signal duration is greater than a preset duration, the pre-processed physiological signals are segmented to obtain multiple sub-physiological signals.

[0130] It should be noted that the pre-processed physiological signals are segmented to obtain multiple sub-physiological signals, which can be segmented in a time-overlapping manner, such as a signal duration of 12 seconds, a signal segmentation of 3 seconds, and four sub-physiological signals of 0-3 seconds, 3-6 seconds, 6-9 seconds and 9-12 seconds. The physiological signals can also be segmented in a time-overlapping manner, and each signal is segmented into a signal segment with a fixed duration (Sig_t), and the overlap length is any value in the range (0-Sig_t-1). For example, a signal duration of 9 seconds, a signal segmentation of 3 seconds, and four sub-physiological signals of 0-3 seconds, 2-5 seconds, 4-7 seconds and 6-9 seconds.

[0131] Further, after the pre-processed physiological signal is subjected to signal segmentation processing to obtain a plurality of sub-physiological signals, the signal quality of each sub-physiological signal can be evaluated, the sub-physiological signal with poor signal quality can be deleted, the signal features are extracted based on the sub-physiological signal with good signal quality, and the effectiveness of the extracted signal features is ensured.

[0132] As one of the embodiments, the signal quality of each sub-physiological signal can be evaluated by calculating the RR interval of the electrocardiogram signal, the KSQI index, the peak interval of the pulse signal, the number of zero-crossing points, the SSQI coefficient of the heart sound signal, the signal mean value, and the like in each sub-physiological signal segment, and the signal segment with noise interference is excluded. These features are input into a classification model such as a SVM (Support Vector Machine) model to coarsely classify each signal, such as classifying into usable sub-physiological signals and unusable sub-physiological signals, and the signal features are extracted based on the usable sub-physiological signals.

[0133] Step D204, for each sub-physiological signal, extracting a sub-signal feature of the sub-physiological signal;

[0134] Step D205, determining a mean signal feature of all the sub-signal features, and taking the mean signal feature as a signal feature of the physiological signal.

[0135] If the signal duration of the pre-processed physiological signal is greater than a preset duration, such as 5 seconds, 6 seconds, 7 seconds, or the like, the pre-processed physiological signal can be subjected to signal segmentation processing, and a sub-signal feature of each sub-physiological signal is extracted.

[0136] It can be understood that the pre-processed physiological signals are subjected to signal segmentation processing, that is, the pre-processed electrocardiogram signals, the pre-processed pulse signals and the pre-processed heart sound signals are subjected to segmentation processing. The sub-signal features of each sub-physiological signal are extracted, and each sub-signal feature constitutes an n-dimensional feature sequence X(t), n is the number of features, the mean value of each dimension feature of all X(t) is calculated to obtain the mean value signal feature, that is, the final signal feature. For example, assuming that the physiological signal includes electrocardiogram signals, pulse signals and heart sound signals, three sub-physiological signals are obtained after segmentation processing, which are sub-physiological signal 1, sub-physiological signal 2 and sub-physiological signal 3. The sub-physiological signal 1 includes sub-electrocardiogram signal 1, sub-heart sound signal 1 and sub-pulse signal 1, the sub-physiological signal 2 includes sub-electrocardiogram signal 2, sub-heart sound signal 2 and sub-pulse signal 2, and the sub-physiological signal 3 includes sub-electrocardiogram signal 3, sub-heart sound signal 3 and sub-pulse signal 3. The signal features of the sub-physiological signal 1 are extracted and recorded as sub-signal features 1 【1_1, 1_2, 1_3】, the signal features of the sub-physiological signal 2 are extracted and recorded as sub-signal features 2 【2_1, 2_2, 2_3】, and the signal features of the sub-physiological signal 3 are extracted and recorded as sub-signal features 3 【3_1, 3_2, 3_3】. The final signal feature obtained is

(1_1+2_1+3_1) / 3, (1_2+2_2+3_2) / 3, (1_3+2_3+3_3) / 3

[0137] In addition, further, after the sub-signal features of each sub-physiological signal are extracted, all the sub-signal features can be subjected to outlier processing to delete abnormal features. Alternatively, for the signal features of each sub-physiological signal, the outlier feature sequence processing is performed in accordance with the box plot principle, and an exemplary processing flow can be as follows: S1, the feature values of each sub-physiological signal feature constitute an n-dimensional feature sequence X(t), n is the number of features; S2, the upper edge and the lower edge of each dimension feature are set, for example, the upper quartile Q1 i , the lower quartile Q3 i , the interquartile range IQR i , the upper edge Q1 i -1.5IQR i , the lower edge Q3 i +1.5IQR i , i=1:n; S3, the abnormal data outside the upper and lower edges of each dimension feature is filtered out; S4, the feature sequence S(t) after removing the outliers is obtained. S5, the mean value of the screened feature sequence is calculated to obtain the mean value signal feature S-.

[0138] The signal features include, but are not limited to, pre-ejection period duration and pulse transit time, and can also include pulse arrival time (PAT), pulse signal and heart sound signal systolic duration ratio, pulse signal and heart sound signal diastolic duration ratio, time domain features, frequency domain features, time-frequency features, statistical features, etc.

[0139] The time domain features include, but are not limited to, the following features: R-R interval (RR) of the electrocardiogram signal, R-R standard deviation (SDNN), root mean square difference (RMSSD), etc.; PP interval (PP) of the pulse signal, half-amplitude pulse width (PW50), systolic time, diastolic time, rise time, fastest rise area, peak height, rise slope, etc.; first zero-crossing time of the VPG signal (a signal obtained by first-order differentiation of the pulse signal), last inflection point time, peak value and first zero-crossing slope, peak slope, peak area, etc.; APG signal (a signal obtained by second-order differentiation of the pulse signal) minimum point time, peak point and minimum point slope, first zero-crossing and minimum point slope; first heart sound duration, second heart sound duration, systolic duration, diastolic duration, etc. in the heart sound signal.

[0140] The frequency domain features include, but are not limited to, the following features: power spectral density of the electrocardiogram signal; first component frequency and its amplitude of the pulse signal, second component frequency and its amplitude, third component frequency and its amplitude; S1 main component frequency of the heart sound signal, S2 main component frequency, etc.

[0141] The time-frequency features mainly include, but are not limited to, the following features: wavelet coefficients, Hilbert Huang transform coefficients, mel-frequency cepstral coefficients, linear prediction coefficient features, etc.

[0142] The statistical features mainly include, but are not limited to, the following features: kurtosis factor, skewness factor, standard deviation of feature sequence, etc.

[0143] Step D20, obtaining systolic duration of the pulse signal and diastolic duration of the pulse signal;

[0144] The signal features of the pulse signal in the systolic period and the diastolic period are different, and based on this, the systolic duration and the diastolic duration are extracted from the pulse signal. The specific extraction method can adopt the prior art, and will not be described herein.

[0145] Step D30, taking the ratio of the systolic duration of the pulse signal to the systolic duration of the heart sound signal as the systolic duration ratio;

[0146] Step D40, taking the ratio of the diastolic duration of the pulse signal to the diastolic duration of the heart sound signal as the diastolic duration ratio.

[0147] Step D50, taking the systolic duration ratio, the diastolic duration ratio and the signal feature as target signal features;

[0148] Step D60, inputting the signal feature into a preset blood pressure prediction model for training, so that the blood pressure prediction model outputs a blood pressure prediction result.

[0149] The trained blood pressure model can be a blood pressure prediction model trained based on a database, the database at least including real blood pressure data and signal feature data corresponding to each real blood pressure data. The database can be obtained by pre-collecting real blood pressure data and signal feature data.

[0150] Further, in order to improve the prediction accuracy of the blood pressure prediction model and realize personalized prediction of blood pressure, the real blood pressure values and physiological signals of the detection object can be collected multiple times in advance to establish an individual data set, and the individual data set is used as a database for training the prediction model to complete the training of the blood pressure prediction model.

[0151] As one of the embodiments, the pre-training process of the blood pressure prediction model can include the following steps: S1, selecting features from a multi-category feature library based on an existing database; S2, calculating mutual information between two features, where p(x) is the probability of occurrence of x, p(y) is the probability of occurrence of y, and p(x, y) is the probability of simultaneous occurrence of x and y, i.e., the joint probability. The higher the mutual information, the higher the dependence between the two features. Remove features with mutual information below a threshold to obtain a new feature subset; S3, calculate the correlation coefficient between the new feature subset and blood pressure, the higher the correlation coefficient, the higher the linear correlation between the feature and blood pressure; S4, sort the feature subset according to the correlation coefficient from high to low to obtain a sorted feature subset; S5, divide the sorted feature subset into a training set and a test set, with a ratio of 8:2; S6, for the training set, use ten-fold cross-validation and backward feature selection to select the number of features to obtain a feature subset S with the lowest RMSE (Root Mean Squared Error); S7, use the final feature subset to train a multiple linear regression model: where BP is a specific blood pressure value, S is the optimal feature subset, Ki is a fitting coefficient of the multiple linear regression model, and n is the dimension of the optimal feature subset.

[0152] In addition, the age, gender, height, weight, BMI (Body Mass Index) index and other personalized characteristics of the tester can be collected, and model training is performed on the signal feature data to obtain a pre-trained personalized blood pressure prediction model. After extracting the signal features based on the physiological signals, the age, gender, height, weight, BMI (Body Mass Index) index and other personalized characteristics of the tester are further detected, the signal features and the personalized characteristics are input into the pre-trained personalized blood pressure prediction model, and the blood pressure prediction result is output. Considering the influence of factors such as age, gender, height, weight, and BMI (Body Mass Index) index on blood pressure, the accuracy of blood pressure prediction can be further improved.

[0153] Further, after obtaining the blood pressure prediction result, the blood pressure prediction result can be output on the wearable device, and the blood pressure prediction result can also be uploaded to a terminal or a server in communication connection with the wearable device for the user to view. The terminal and the server can realize the user's historical blood pressure data viewing, which helps doctors diagnose. According to the historical data, a personalized blood pressure measurement scheme is customized to help users better understand their own health status, and then more scientific user treatment opinions are given.

[0154] In the embodiment, the physiological signals of the detection object are collected, wherein the physiological signals include electrocardiogram signals, pulse signals and heart sound signals; the signal features are extracted from the physiological signals to obtain signal features, wherein the signal features include the pre-ejection period duration and the pulse transit time; and the signal features are input into a pre-trained blood pressure prediction model to output a blood pressure prediction result by the blood pressure prediction model. Compared with the prior art of estimating blood pressure based on the pulse transit time, the embodiment predicts the blood pressure of the tester based on the fusion signal features such as the pre-ejection period duration and the pulse transit time, and considers the influence of the pre-ejection period duration on the blood pressure, thereby improving the prediction accuracy of the blood pressure.

[0155] In the embodiment, after the heart sound signal is segmented, the second systolic duration and the second diastolic duration based on the heart sound signal are obtained, and the first systolic duration and the first diastolic duration of the pulse signal are obtained. The ratio between the two is also used as a signal feature, so as to comprehensively measure the diastolic duration and the systolic duration of the heart sound signal and the pulse signal, and the prediction accuracy of the blood pressure can be further improved.

[0156] In a possible implementation, the step of extracting features from the physiological signals includes:

[0157] Step E10, determining a QRS peak of the electrocardiogram signal, determining a heart sound peak of the heart sound signal, determining a time difference between the two peaks, and taking the time difference as the duration of the pre-ejection period;

[0158] It should be noted that if the physiological signal is segmented, the feature extraction of the physiological signal in this embodiment can actually be the feature extraction of each sub-physiological signal, and the electrocardiogram signal, the heart sound signal and the pulse signal are also included in the electrocardiogram signal, the heart sound signal and the pulse signal of the sub-physiological signal.

[0159] It can be understood that, as shown in FIG. 4, the physiological signal includes an electrocardiogram (ECG) signal, a phonocardiogram (PCG) signal and a pulse signal. The signal peak of the electrocardiogram signal is determined, and the signal peak of the heart sound signal is determined. Specifically, the QRS peak of the electrocardiogram signal belonging to the same heartbeat cycle, the heart sound peak of the heart sound signal, the time corresponding to the two signal peaks is obtained, and the time difference between the two is taken as the duration of the pre-ejection period. If the physiological signal includes multiple heartbeat cycles, the duration of the pre-ejection period corresponding to each heartbeat cycle can be determined, and the average of all the durations of the pre-ejection period is taken as the final duration of the pre-ejection period.

[0160] Step E20, obtaining a pulse transit time based on the pulse signal and the heart sound signal.

[0161] Taking the S1 peak point of the heart sound signal as the starting point and the pulse wave feature point as the ending point, the time difference between the two is the pulse transit time. Therefore, one or more pulse wave feature points can be selected to obtain one or more corresponding pulse transit times, that is, the pulse transit time includes one or more. The user can set the selection rule of the pulse wave feature point according to the actual situation, and the number of features of the pulse transit time is not specifically limited in this embodiment.

[0162] Similarly, based on the synchronous acquisition of the electrocardiogram signal and the pulse signal, the R wave peak of the electrocardiogram signal is taken as the starting point, and one or more pulse arrival times (PAT) are determined. The one or more pulse arrival times can also be taken as the extracted signal features.

[0163] In this embodiment, the fusion signal features such as the duration of the pre-ejection period and the pulse transit time are extracted, which provides an effective data basis for predicting the blood pressure value.

[0164] In order to help understand the technical concept or technical principle of the present application, a specific embodiment is listed:

[0165] Referring to FIGS. 5-6, the blood pressure prediction process in this specific embodiment is as follows:

[0166] The wristwatch type device is started through a device switch, and the wristwatch type device can be connected with a server and a mobile phone APP. After time calibration, a user can input personalized information such as height, weight and the like. The user is prompted to wear the wristwatch type device, and after the user wears the wristwatch type device according to the prompt information, the wristwatch type device starts blood pressure measurement, and physiological signals including electrocardio signals, heart sound signals and pulse signals are synchronously collected. In addition, acceleration and angular velocity information of the user is collected, and the current Euler angle of the user is calculated to assist the user in positioning the collection position and posture. During the blood pressure measurement process, the acceleration and angular velocity information of the user is collected to monitor the movement of the arm of the user. In combination with the electrocardio signals, the heart sound signals and the pulse signals, the physiological signals are denoised, and the collected physiological signals are processed, such as filtering (which can specifically include band-pass filtering and adaptive filtering), signal quality evaluation and the like. The evaluation result of the signal quality evaluation is used for feature extraction of physiological signals with high signal quality. The extracted signal features are input into a blood pressure prediction model to perform blood pressure prediction, and the blood pressure prediction result is displayed and uploaded.

[0167] It should be noted that the above specific embodiments are only used to understand the present application, and do not constitute a limitation on the blood pressure prediction process of the present application and a limitation on the application device. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0168] Embodiment four

[0169] The embodiment of the present application also provides a heart sound segmentation device, please refer to figure 7, the heart sound segmentation device includes:

[0170] The signal collection module is used for synchronously collecting physiological signals, wherein the physiological signals include heart sound signals.

[0171] The segmentation module is used for obtaining a segmentation threshold corresponding to the heart sound signals, segmenting the heart sound signals based on the segmentation threshold, and obtaining first heart sounds and second heart sounds.

[0172] The determination module is used for determining the systolic duration and the diastolic duration according to the first heart sounds and the second heart sounds.

[0173] The physiological signals also include electrocardio signals and pulse signals. The segmentation module is also used for:

[0174] Determining a QRS wave peak point of the electrocardio signals, taking a time corresponding to the QRS wave peak point as a first time.

[0175] Determining a wave trough point of the pulse signals, and taking a time corresponding to the wave trough point as a second time.

[0176] determining a first zero-crossing point and a second zero-crossing point of the pulse signal, taking a time corresponding to the first zero-crossing point as a third time, and taking a time corresponding to the second zero-crossing point as a fourth time;

[0177] determining a first threshold value of a heart sound signal corresponding to the first time and the second time, and determining a second threshold value of the heart sound signal corresponding to the third time and the fourth time;

[0178] taking the first threshold value and the second threshold value as a segmentation threshold value.

[0179] The segmentation module is further configured to:

[0180] obtain or determine a heart sound envelope signal corresponding to the heart sound signal;

[0181] take a heart sound peak value of the heart sound envelope signal between the first time and the second time as a first heart sound peak value;

[0182] adjust the first heart sound peak value based on a preset adjustment coefficient to obtain a first threshold value, wherein the first threshold value is less than the first heart sound peak value.

[0183] The segmentation module is further configured to:

[0184] obtain or determine a heart sound envelope signal corresponding to the heart sound signal;

[0185] take a heart sound peak value of the heart sound envelope signal between the third time and the fourth time as a second heart sound peak value;

[0186] adjust the second heart sound peak value based on a preset adjustment coefficient to obtain a second threshold value, wherein the second threshold value is less than the second heart sound peak value.

[0187] The segmentation threshold value includes a first threshold value and a second threshold value, and the segmentation module is further configured to:

[0188] obtain or determine a heart sound envelope signal corresponding to the heart sound signal;

[0189] take a duration window in which a signal value of the heart sound envelope signal is greater than the first threshold value as a first duration window;

[0190] take a duration window in which a signal value of the heart sound envelope signal is greater than the second threshold value as a second duration window;

[0191] take a heart sound signal in the first duration window as a first heart sound, and take a heart sound signal in the second duration window as a second heart sound.

[0192] The determination module is further configured to:

[0193] determining a cardiac cycle included in the heart sound signal, sequentially traversing each of the cardiac cycles;

[0194] taking an ending time of the first heart sound in the traversed cardiac cycle as a first ending time, determining a next cardiac cycle corresponding to the traversed cardiac cycle, and taking a starting time of the first heart sound in the next cardiac cycle as a first starting time;

[0195] taking a starting time of the second heart sound in the traversed cardiac cycle as a second starting time, and taking an ending time of the second heart sound in the traversed cardiac cycle as a second ending time;

[0196] taking a time difference between the first ending time and the second starting time as a systolic duration;

[0197] taking a time difference between the first starting time and the second ending time as a diastolic duration;

[0198] until the traversal of each of the cardiac cycles is completed, determining an average systolic duration of all of the systolic durations, and determining an average diastolic duration of all of the diastolic durations;

[0199] taking the average systolic duration as a systolic duration of the heart sound signal, and taking the average diastolic duration as a diastolic duration of the heart sound signal.

[0200] The heart sound segmentation device further comprises a blood pressure prediction module, and the blood pressure prediction module is configured to:

[0201] extracting features of the physiological signal to obtain signal features, wherein the signal features comprise a pre-ejection period duration and a pulse transit time;

[0202] obtaining a systolic duration of the pulse signal and a diastolic duration of the pulse signal;

[0203] taking a ratio of the systolic duration of the pulse signal to the systolic duration of the heart sound signal as a systolic duration ratio;

[0204] taking a ratio of the diastolic duration of the pulse signal to the diastolic duration of the heart sound signal as a diastolic duration ratio;

[0205] taking the systolic duration ratio, the diastolic duration ratio, and the signal features as target signal features;

[0206] inputting the signal features into a preset blood pressure prediction model for training, so that the blood pressure prediction model outputs a blood pressure prediction result.

[0207] The blood pressure prediction mode is also used for:

[0208] determining a QRS peak of the electrocardio signal, determining a heart sound peak of the heart sound signal, determining a time difference between the two peaks, and taking the time difference as a pre-ejection period duration;

[0209] acquiring a pulse transit time based on the pulse signal and the heart sound signal.

[0210] In addition, the signal acquisition module is also used for:

[0211] acceleration and gyroscope signals during user measurement.

[0212] The signal acquisition module includes an electrocardio measurement module, a pulse measurement module, a heart sound signal measurement module, and a 6-axis signal acquisition module.

[0213] The electrocardio signal is mainly acquired by the electrocardio measurement module, which is mainly composed of a high-impedance chip and three dry electrodes with good conductivity. The pulse signal is mainly acquired by the pulse measurement module, which is mainly composed of a multi-wavelength LED (light-emitting diode), a photoelectric sensor, and a filter amplification circuit. The final pulse signal is fused by the multi-wavelength LED. To ensure the volume of the cuffless blood pressure measurement watch, the high-impedance characteristics of the simulated electrocardio signal acquisition, the fusion of the pulse signal, the filter amplification circuit, and the synchronization of the two physiological signals are all realized by the integrated AFE chip.

[0214] The heart sound signal is mainly acquired by the heart sound signal measurement module, which is mainly composed of a digital VPU sensor. The VPU sensor is attached to the inside of the watch shell through a strict rigid connection method, and the amplification multiple of the VPU sensor is adjusted to prevent signal overflow during testing, so as to obtain complete heart sound signals.

[0215] The acceleration and gyroscope signals are mainly acquired by the 6-axis signal acquisition module, which is mainly composed of a 6-axis sensor integrated inside the watch. Before blood pressure measurement, the 6-axis signal acquisition module can calculate the current Euler angle to assist the user in positioning the collection position and posture. During blood pressure measurement, the 6-axis signal acquisition module can monitor the movement of the user's arm, combine with the electrocardio signal, the heart sound signal, and the pulse signal to denoise the physiological signals, and improve the signal-to-noise ratio of the above signals during collection.

[0216] In addition, the heart sound segmentation device also includes:

[0217] A watch case for placing sensors for collecting physiological signals and a watch control system;

[0218] A time calibration module for displaying real-time time and calibrating data synchronization collection;

[0219] An interaction module for collecting personalized information of a user, responding to signals generated by the user during blood pressure measurement operation, and providing simple blood pressure measurement watch use instructions. The signals generated during blood pressure measurement operation include a blood pressure measurement start signal. The watch use instructions include blood pressure measurement watch signal collection positions and user collection postures.

[0220] A data processing module for processing physiological signal segments collected by the physiological signal measurement module in real time. The data processing steps mainly include signal noise reduction, signal quality evaluation, and feature extraction.

[0221] A wireless communication module for transmitting collected physiological signal data, real user blood pressure values, and user personalized information to a server or a mobile terminal through a wireless module for blood pressure measurement watch database construction.

[0222] The watch case includes a watch switch and three dry electrodes for electrocardiogram monitoring. The watch switch mainly controls the on-off of the watch and the retrieval of the internal menu of the watch. The first electrode and the third electrode form a loop for electrocardiogram signal collection, and the second electrode provides a reference point to eliminate the potential difference between the body and the watch, thereby improving the signal-to-noise ratio of the electrocardiogram signal collection. The watch case includes rigidly connected VPU sensors and photoelectric pulse sensors for heart sound signal and photoplethysmogram signal measurement.

[0223] The time calibration module is used to generate a real-time clock, control the sensors to start synchronous physiological signal collection at the same time, determine the relationship between the electrocardiogram signal, pulse signal, and heart sound signal and time, and ensure the strict synchronization of the physiological signals. The calibrated real-time time is used to record the time when the data collection starts.

[0224] The interaction module includes an input module, a display module, and a prompt module, and their functions are realized by the internal MCU (Microcontroller Unit) of the watch and the watch screen. The input module 141 in the interaction module is used to input personalized information into the internal watch through the screen of the watch before blood pressure measurement, including personal information such as gender, height, age, and weight, and information such as whether to take antihypertensive drugs and the name of the antihypertensive drugs.

[0225] The display module in the interaction module is used for displaying the watch using method and the measurement steps.

[0226] The prompt module in the interaction module includes a voice chip and a linear vibration motor, and is used for prompting the user operation flow, the arm placement position and the measurement posture, and ensuring the blood pressure measurement accuracy.

[0227] The data processing module includes a signal noise reduction module and a signal quality evaluation module.

[0228] The signal noise reduction module is used for performing normalization operation on the collected physiological signals, and further performing band-pass filtering on the electrocardio signals, the pulse signals and the heart sound signals through a pre-designed FIR (Finite Impulse Response) band-pass filter.

[0229] The signal quality evaluation module is used for dividing each signal into a signal segment with a fixed time length (Sig_t), and the time length is generally > 5s, and the overlapping length is an arbitrary value in the range of (0-Sig_t-1).

[0230] The wireless communication module is used for sending the collected physiological signals, the input personalized information and the actually measured blood pressure value to a terminal and uploading to a cloud server for data collection.

[0231] The heart sound segmentation device provided by the application can solve the technical problem of reducing the resource consumption of heart sound segmentation of heart sound signals.

[0232] Embodiment five

[0233] An electronic device is provided, and the electronic device includes at least one processor, and a memory connected to the at least one processor in communication. The memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, enable the at least one processor to perform the heart sound segmentation method in Embodiment 1.

[0234] Reference is made below to FIG. 8, which shows a structural schematic diagram of an electronic device suitable for implementing embodiments of the present disclosure. The electronic device in embodiments of the present disclosure can be a wearable device or the like. The electronic device shown in FIG. 8 is merely an example, and should not impose any limitation on the functions and use range of embodiments of the present disclosure.

[0235] As shown in FIG. 8, the electronic device can include a processing apparatus 1001 (such as a central processor, a graphics processor, or the like), which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM 1002) or loaded from a storage apparatus into a random access memory (RAM 1004). In the RAM 1004, various programs and data required for operation of the electronic device are also stored. The processing apparatus 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface is also connected to the bus 1005.

[0236] Generally, the following systems can be connected to the I / O interface 1006: input apparatus 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, or the like; output apparatus 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, or the like; storage apparatus 1003 including, for example, a magnetic tape, a hard disk, or the like; and communication apparatus 1009. The communication apparatus 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although an electronic device having various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can alternatively be implemented or provided.

[0237] In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication apparatus, or installed from the storage apparatus 1003, or installed from the ROM 1002. When the computer program is executed by the processing apparatus 1001, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

[0238] The electronic device provided by the present application adopts the heart sound segmentation method in the above embodiment, and can solve the technical problem of how to reduce the resource consumption of heart sound segmentation of the heart sound signal. Compared with the prior art, the electronic device provided by the embodiment of the present application has the same beneficial effects as the heart sound segmentation method provided by the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0239] It should be understood that parts of the present disclosure can be realized by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0240] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0241] Embodiment six

[0242] The embodiment of the present application provides a computer readable storage medium having computer readable program instructions stored thereon, and the computer readable program instructions are used to execute the heart sound segmentation method in the above embodiment one.

[0243] The computer readable storage medium provided by the embodiment of the present application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system or device, or any combination of the above. More specific examples of computer readable storage media can include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer readable storage medium can be transmitted by any appropriate medium, including but not limited to: electric wire, optical cable, RF (radio frequency), etc., or any suitable combination of the above.

[0244] The above computer readable storage medium can be contained in the electronic device; or can exist separately without being assembled into the electronic device.

[0245] The computer readable storage medium described above carries one or more programs, when the one or more programs are executed by the electronic device, the electronic device is caused to: synchronously collect a physiological signal, wherein the physiological signal includes a heart sound signal; acquire a segmentation threshold corresponding to the heart sound signal, segment the heart sound signal based on the segmentation threshold to obtain a first heart sound and a second heart sound; and determine a systolic duration and a diastolic duration according to the first heart sound and the second heart sound.

[0246] Computer program code for carrying out operations of the present disclosure can be written in any one or more of a variety of programming languages or combinations of languages, including an object-oriented programming language such as Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0247] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can 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

[0248] The modules described in the embodiments of the present disclosure can be implemented by software, or can be implemented by hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0249] The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions for executing the heart sound segmentation method, and can solve the technical problem of how to reduce the resource consumption of heart sound segmentation of heart sound signals. Compared with the prior art, the computer readable storage medium provided by the embodiment of the application has the same beneficial effects as the heart sound segmentation method provided by the first embodiment, the second embodiment or the third embodiment, and will not be repeated here.

[0250] Embodiment seven

[0251] The embodiment of the application further provides a computer program product, comprising a computer program, and the computer program is executed by a processor to realize the steps of the heart sound segmentation method.

[0252] The computer program product provided by the application can solve the technical problem of how to reduce the resource consumption of heart sound segmentation of heart sound signals. Compared with the prior art, the computer program product provided by the embodiment of the application has the same beneficial effects as the heart sound segmentation method provided by the first embodiment, the second embodiment or the third embodiment, and will not be repeated here.

[0253] The above is only the preferred embodiment of the application, and does not limit the patent scope of the application, and any equivalent structure or equivalent process transformation using the content of the specification and the drawings, or direct or indirect application in other related technical fields, are also included in the patent processing scope of the application.

Claims

1. A heart sound segmentation method, characterized by, The heart sound segmentation method comprises: synchronously collecting physiological signals, wherein the physiological signals comprise heart sound signals; obtaining a segmentation threshold corresponding to the heart sound signals, and segmenting the heart sound signals based on the segmentation threshold to obtain first heart sounds and second heart sounds; determining the systolic duration of the heart sound signals and the diastolic duration of the heart sound signals according to the first heart sounds and the second heart sounds.

2. The heart sound segmentation method of claim 1, wherein, The physiological signals further comprise electrocardiogram signals and pulse signals, and before the step of obtaining the segmentation threshold corresponding to the heart sound signals, the method further comprises: determining a QRS wave peak point of the electrocardiogram signals, and taking the time corresponding to the QRS wave peak point as a first time; determining a wave trough point of the pulse signals, and taking the time corresponding to the wave trough point as a second time; determining a first zero-crossing point and a second zero-crossing point of the pulse signals, taking the time corresponding to the first zero-crossing point as a third time, and taking the time corresponding to the second zero-crossing point as a fourth time; determining a first threshold corresponding to the first time and the second time, and determining a second threshold corresponding to the third time and the fourth time; taking the first threshold and the second threshold as the segmentation threshold.

3. The heart sound segmentation method of claim 2, wherein, The step of determining the first threshold corresponding to the first time and the second time comprises: obtaining or determining a heart sound envelope signal corresponding to the heart sound signals; taking a heart sound peak value of the heart sound envelope signal between the first time and the second time as a first heart sound peak value; adjusting the first heart sound peak value based on a preset adjustment coefficient to obtain a first threshold, wherein the first threshold is smaller than the first heart sound peak value.

4. The heart sound segmentation method of claim 2, wherein, The step of determining the second threshold corresponding to the third time and the fourth time comprises: obtaining or determining a heart sound envelope signal corresponding to the heart sound signals; taking a heart sound peak value of the heart sound envelope signal between the third time and the fourth time as a second heart sound peak value; adjusting the second heart sound peak value based on a preset adjustment coefficient to obtain a second threshold, wherein the second threshold is smaller than the second heart sound peak value.

5. The heart sound segmentation method of claim 1, wherein, The segmentation threshold comprises the first threshold and the second threshold, and the step of segmenting the heart sound signals based on the segmentation threshold comprises: obtaining or determining a heart sound envelope signal corresponding to the heart sound signals; taking a duration window in which the signal value of the heart sound envelope signal is greater than the first threshold as a first duration window; taking a duration window in which the signal value of the heart sound envelope signal is greater than the second threshold as a second duration window; taking the heart sound signals in the first duration window as the first heart sounds, and taking the heart sound signals in the second duration window as the second heart sounds.

6. The heart sound segmentation method of claim 1, wherein, The step of determining the systolic duration of the heart sound signals and the diastolic duration of the heart sound signals according to the first heart sounds and the second heart sounds comprises: determining a heart cycle included in the heart sound signals, and sequentially traversing each heart cycle. The end time of the first heart sound in the traversed cardiac cycle is taken as a first end time, a next cardiac cycle corresponding to the traversed cardiac cycle is determined, and the start time of the first heart sound in the next cardiac cycle is taken as a first start time; The start time of the second heart sound in the traversed cardiac cycle is taken as a second start time, and the end time of the second heart sound in the traversed cardiac cycle is taken as a second end time; The time difference between the first end time and the second start time is taken as a systolic duration; The time difference between the first start time and the second end time is taken as a diastolic duration; After each of the cardiac cycles is traversed, an average systolic duration of all the systolic durations is determined, and an average diastolic duration of all the diastolic durations is determined; The average systolic duration is taken as a systolic duration of a heart sound signal, and the average diastolic duration is taken as a diastolic duration of the heart sound signal.

7. A heart sound segmentation apparatus characterized by comprising: The heart sound segmentation device comprises: a signal acquisition module configured to acquire a physiological signal, wherein the physiological signal comprises a heart sound signal; a segmentation module configured to obtain a segmentation threshold corresponding to the heart sound signal, and segment the heart sound signal based on the segmentation threshold to obtain a first heart sound and a second heart sound; a determination module configured to determine a systolic duration of the heart sound signal and a diastolic duration of the heart sound signal according to the first heart sound and the second heart sound.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the heart sound segmentation method according to any one of claims 1 to 6.

9. A readable storage medium, characterized by, The readable storage medium is a computer readable storage medium, and the computer readable storage medium stores a program for implementing a heart sound segmentation method. The program for implementing the heart sound segmentation method is executed by a processor to implement the steps of the heart sound segmentation method according to any one of claims 1 to 6.

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