CN116344056A

CN116344056A - Data processing method and device

Info

Publication number: CN116344056A
Application number: CN202310370593.XA
Authority: CN
Inventors: 迟新一; 杨斌; 施宇; 王劲君
Original assignee: Beijing Calorie Information Technology Co ltd; Hangzhou Sports Co ltd
Current assignee: Beijing Calorie Information Technology Co ltd; Hangzhou Sports Co ltd
Priority date: 2023-04-07
Filing date: 2023-04-07
Publication date: 2023-06-27

Abstract

The specification provides a data processing method and device, wherein the data processing method is applied to a client, and comprises the following steps: acquiring initial heart rate data corresponding to a target user, and determining initial curve crest information corresponding to the initial heart rate data; creating a data window aiming at the initial curve crest information, carrying out interpolation processing on the data window, and generating target heart rate data according to a processing result; determining target curve crest information corresponding to the target heart rate data, and constructing a peak-to-peak interval sequence according to the target curve crest information; and updating the peak interval sequence into a target peak interval sequence, and calculating the heart rate variability of the target user by utilizing the peak interval parameter contained in the target peak interval sequence, so that the calculation accuracy of the heart rate variability is effectively improved.

Description

Data processing method and device

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method and apparatus.

Background

With the increasing importance of users on health, more and more health information monitoring devices are applied, and most of the devices accurately feed back health information of users at any moment, collect and feed back heart rate information, so as to reflect physical conditions of the users at any moment. While the rhythm of healthy heart beats is not regular, describing such irregularities in the heart typically uses an indicator of heart rate variability (heartrate variability, HRV). Specifically, in a continuous Electrocardiographic (ECG) signal, the position of each R peak represents that a heartbeat occurs, and an RR interval sequence can be obtained by calculating the interval between two adjacent R peaks, where HRV describes the characteristics obtained by performing a time domain, frequency domain, nonlinear analysis method on the RR interval sequence. In the prior art, when determining heart rate variability information of a user at the current moment, most of the equipment is completed by acquiring an Electrocardiogram (ECG) signal, and an RR interval sequence is acquired by replacing the ECG signal with a photoplethysmogram pulse wave (PPG) signal so as to calculate heart rate variability; analysis of heart rate variability typically suggests the use of higher frequency signals, whereas most devices capable of monitoring user health information do not support the acquisition of high frequency signals and have limited computational power, and therefore an effective solution is needed to solve the above-mentioned problems.

Disclosure of Invention

In view of this, the present embodiments provide a data processing method. The present disclosure is also directed to a data processing apparatus, a computing device, and a computer-readable storage medium that address the technical shortcomings of the prior art.

According to a first aspect of embodiments of the present disclosure, there is provided a data processing method, applied to a client, including:

acquiring initial heart rate data corresponding to a target user, and determining initial curve crest information corresponding to the initial heart rate data;

creating a data window aiming at the initial curve crest information, carrying out interpolation processing on the data window, and generating target heart rate data according to a processing result;

determining target curve crest information corresponding to the target heart rate data, and constructing a peak-to-peak interval sequence according to the target curve crest information;

and updating the peak interval sequence into a target peak interval sequence, and calculating the heart rate variability of the target user by utilizing the peak interval parameters contained in the target peak interval sequence.

Optionally, the determining initial curve peak information corresponding to the initial heart rate data includes:

Preprocessing the initial heart rate data, and determining curve information corresponding to the initial heart rate data according to a preprocessing result;

and generating initial curve crest information corresponding to the initial heart rate data based on the curve information.

Optionally, the preprocessing the initial heart rate data, determining curve information corresponding to the initial heart rate data according to a preprocessing result, includes:

the initial heart rate data are subjected to segmentation processing according to a first set duration to obtain a plurality of initial heart rate data segments, wherein any two adjacent initial heart rate data segments in the plurality of initial heart rate data segments have overlapping intervals corresponding to a second set duration, and the second set duration is smaller than the first set duration;

carrying out band-pass filtering on each initial heart rate data segment, and carrying out normalization processing on each initial heart rate data segment after the band-pass filtering;

determining the sub-curve information of each initial heart rate data segment according to the normalization processing result, and determining the curve information corresponding to the initial heart rate data based on the sub-curve information of each initial heart rate data segment.

Optionally, the generating initial curve peak information corresponding to the initial heart rate data based on the curve information includes:

Calculating curve characteristic information corresponding to the initial heart rate data according to the curve information, wherein the curve characteristic information comprises a characteristic value of each curve peak in a plurality of curve peaks;

comparing the characteristic value of each curve peak with a characteristic threshold value, and determining an initial curve peak in the plurality of curve peaks according to a comparison result;

and determining initial curve crest information corresponding to the initial heart rate data based on the initial curve crest.

Optionally, the creating a data window for the initial curve peak information, performing interpolation processing on the data window, and generating target heart rate data according to a processing result includes:

determining an initial peak point corresponding to the initial heart rate data according to the initial curve peak information;

selecting a set number of front adjacent sampling points and rear adjacent sampling points which are adjacent to the initial peak point in the initial heart rate data;

creating the data window according to the initial peak sampling point, the front adjacent sampling point and the rear adjacent sampling point;

and processing the data window according to a preset interpolation algorithm, and generating the target heart rate data according to a processing result.

Optionally, the constructing a peak-to-peak interval sequence according to the target curve peak information includes:

acquiring a target data window corresponding to the target curve crest information and a target data segment to which the target curve crest information belongs;

determining first relative position information of the target curve peak information with respect to the target heart rate data and second relative position information of the target data window with respect to the target data segment;

calculating absolute position information of the target curve crest information in the target heart rate data according to the first relative position information and the second relative position information;

the peak-to-peak interval sequence is constructed based on the absolute position information.

Optionally, the constructing the peak-to-peak interval sequence based on the absolute position information includes:

constructing an absolute position sequence according to the absolute position information;

updating the absolute position sequence based on the overlapping position information to obtain a target absolute position sequence when the overlapping position information is contained in the absolute position sequence;

the peak-to-peak interval sequence is obtained by performing differential processing on target absolute position information contained in the target absolute position sequence.

Optionally, the updating the peak-to-peak interval sequence to the target peak-to-peak interval sequence includes:

traversing the initial peak interval parameters contained in the peak interval sequence, and determining abnormal peak interval parameters according to the traversing result;

updating the peak-to-peak interval sequence based on the abnormal peak-to-peak interval parameter to obtain the target peak-to-peak interval sequence.

Optionally, the updating the peak-to-peak interval sequence based on the abnormal peak-to-peak interval parameter to obtain the target peak-to-peak interval sequence includes:

determining a first correction threshold and a second correction threshold according to a preset correction strategy;

determining a first peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter in the peak-to-peak interval sequence if the abnormal peak-to-peak interval parameter is greater than the first correction threshold and the second correction threshold;

screening associated peak-to-peak interval parameters from the peak-to-peak interval sequence according to the abnormal peak-to-peak interval parameters and the first peak-to-peak interval parameters;

updating the abnormal peak interval parameter and the associated peak interval parameter based on the second peak interval parameter and the first peak interval parameter associated with the abnormal peak interval parameter, and generating the target peak interval sequence according to an updating result.

determining a third correction threshold and a fourth correction threshold according to a preset correction strategy;

determining a third peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter in the peak-to-peak interval sequence if the abnormal peak-to-peak interval parameter is less than the third correction threshold and the fourth correction threshold;

calculating an updated peak-to-peak interval parameter based on the abnormal peak-to-peak interval parameter and the third peak-to-peak interval parameter;

and under the condition that the updated peak-to-peak interval parameter meets a preset parameter threshold, updating the abnormal peak-to-peak interval parameter by using the updated peak-to-peak interval parameter, and generating the target peak-to-peak interval sequence according to an updating result.

According to a second aspect of embodiments of the present specification, there is provided a data processing apparatus, for application to a client, comprising:

the acquisition module is configured to acquire initial heart rate data corresponding to a target user and determine initial curve crest information corresponding to the initial heart rate data;

the creation module is configured to create a data window aiming at the initial curve crest information, perform interpolation processing on the data window and generate target heart rate data according to a processing result;

The determining module is configured to determine target curve crest information corresponding to the target heart rate data and construct a peak-to-peak interval sequence according to the target curve crest information;

and the calculating module is configured to update the peak interval sequence to a target peak interval sequence and calculate heart rate variability of the target user by using peak interval parameters contained in the target peak interval sequence.

According to a third aspect of embodiments of the present specification, there is provided a computing device comprising:

a memory and a processor;

the memory is configured to store computer-executable instructions that, when executed, implement the steps of the data processing method.

According to a fourth aspect of embodiments of the present description, there is provided a computer-readable storage medium storing computer-executable instructions which, when executed by a processor, implement the steps of the data processing method.

According to the data processing method provided by the specification, in order to improve the determination accuracy of heart rate variability and not consume more calculation resources, initial heart rate data corresponding to a target user can be acquired first, initial curve peak information is determined based on the initial heart rate data, then a data window is created for the initial curve peak information, interpolation processing is carried out on the data window, so that the target heart rate data is obtained, after the client collects the initial heart rate data with lower frequency, the initial heart rate data can be improved to be high frequency capable of calculating heart rate variability in an interpolation mode, calculation of heart rate variability is carried out based on the high frequency, calculation accuracy can be effectively improved, more types of clients can be adapted, and universality is higher. After that, the peak information of the target curve corresponding to the target heart rate data can be determined, then the peak interval sequence is constructed according to the peak information of the target curve, and the peak interval sequence is updated as the target peak interval sequence based on the peak information, so that the peak interval parameters which are not matched with the real heart rate variation of the user are removed, finally, the heart rate variability of the target user is calculated by utilizing the peak interval parameters contained in the target peak interval sequence, the calculation accuracy of the heart rate variability is effectively improved, meanwhile, the consumption of calculation resources can be effectively reduced, the accurate calculation of the heart rate variability can be completed on any type of equipment, the monitoring requirement of the health information of the user is met, and more stable and accurate health management service is provided for the user.

Drawings

FIG. 1 is a flow chart of a data processing method according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a data processing method according to an embodiment of the present disclosure;

FIG. 3 is a process flow diagram of a data processing method according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a data processing apparatus according to an embodiment of the present disclosure;

fig. 5 is a block diagram of a computing device according to an embodiment of the present disclosure.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many other forms than described herein and similarly generalized by those skilled in the art to whom this disclosure pertains without departing from the spirit of the disclosure and, therefore, this disclosure is not limited by the specific implementations disclosed below.

The terminology used in the one or more embodiments of the specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the specification. As used in this specification, one or more embodiments and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any or all possible combinations of one or more of the associated listed items.

It should be understood that, although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first may also be referred to as a second, and similarly, a second may also be referred to as a first, without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.

First, terms related to one or more embodiments of the present specification will be explained.

Heart rate variability: HRV, which is the change of the beat-to-beat cycle difference, contains information of the regulation of the cardiovascular system by neurohumoral factors, so that the judgment of the disease conditions and prevention of cardiovascular diseases and other diseases is a valuable index for predicting sudden cardiac death and arrhythmia events.

ECG signal: the (electromagnogram) is a signal for recording the pattern of changes in electrical activity produced by the heart for each cardiac cycle from the body surface using an electrocardiograph.

PPG signal: is a signal obtained by detecting the exercise heart rate of a human body by using a photoplethysmography (PPG) technology.

In the present specification, a data processing method is provided, and the present specification relates to a data processing apparatus, a computing device, and a computer-readable storage medium, which are described in detail in the following embodiments one by one.

In practice, HRV analysis usually suggests using ECG signals above 1000Hz, but PPG signals as an alternative to ECG are usually not configured to 1000Hz in view of power consumption, computing power, and storage space of the wearable device, since they are usually obtained from photosensors deployed on the wearable device. The lower sampling rate may result in lower time resolution of the sampling points, i.e. the time difference (1/sampling rate) between the sampling points is too large, which further affects the accuracy of the RR interval value and the calculation of the HRV characteristics. Meanwhile, due to the influence of factors such as signal quality and peak-finding algorithm performance, in continuous PPG recording, some peak points which cannot represent heart beat are usually missed or mistakenly identified additionally, which results in that some abnormal RR interval values occur in the RR interval sequence obtained through calculation, and these abnormal values have great interference on HRV analysis, so that an effective scheme is needed to solve the above problems.

Referring to the schematic diagram shown in fig. 1, in order to improve the accuracy of determining the heart rate variability and not consume more computing resources, the data processing method provided in the present disclosure may first obtain initial heart rate data corresponding to a target user, determine initial curve peak information based on the initial heart rate data, then create a data window for the initial curve peak information, and perform interpolation processing on the data window, thereby obtaining target heart rate data, and after the client collects the initial heart rate data with a low frequency, the initial heart rate data may be lifted to a high frequency capable of computing the heart rate variability by an interpolation method, and based on this, computing accuracy may be effectively improved, and the method may be adapted to more types of clients, and has stronger versatility. After that, the peak information of the target curve corresponding to the target heart rate data can be determined, then the peak interval sequence is constructed according to the peak information of the target curve, and the peak interval sequence is updated as the target peak interval sequence based on the peak information, so that the peak interval parameters which are not matched with the real heart rate variation of the user are removed, finally, the heart rate variability of the target user is calculated by utilizing the peak interval parameters contained in the target peak interval sequence, the calculation accuracy of the heart rate variability is effectively improved, meanwhile, the consumption of calculation resources can be effectively reduced, the accurate calculation of the heart rate variability can be completed on any type of equipment, the monitoring requirement of the health information of the user is met, and more stable and accurate health management service is provided for the user.

Fig. 2 shows a flowchart of a data processing method according to an embodiment of the present disclosure, where the method is applied to a client, and specifically includes the following steps:

step S202, initial heart rate data corresponding to a target user are obtained, and initial curve crest information corresponding to the initial heart rate data is determined.

The data processing method provided in this embodiment is applied to a client, where the client may be a wearable device of a user, a held terminal, or a health information testing device, such as a bracelet, a mobile phone, or a tester (blood pressure tester, blood oxygen tester), etc.

Specifically, the target user specifically refers to a user who needs to hold a client and needs to perform a heart rate variability test, such as a patient, a user in exercise, and the like; correspondingly, the initial heart rate data specifically refers to an electrocardiosignal collected by a photoelectric sensor configured by the client, such as a photoplethysmography (PPG) signal of the wearable device, and it is required to be noted that the sampling rate of the initial heart rate data when collected is lower, so as to realize collection by utilizing the self collection capability of the client, and avoid using a sensor with higher cost. Correspondingly, the initial curve peak information specifically refers to position information of each curve peak in a heart rate change curve constructed based on initial heart rate data, and the position information is used for representing information such as a heart beat rule of a user.

Based on the above, in order to avoid loss to the client and improve the calculation accuracy of heart rate variability, the photoelectric sensor of the client may collect initial heart rate data, such as PPG electrocardiosignal, of the user at the current moment; on the basis, because the heart beat rule of the user is the basis for calculating the heart rate variability, the heart beat rule and other information can be represented by the graph, the heart rate graph corresponding to the user can be drawn according to the initial heart rate data, then the peak position of each heart beat rule capable of representing the user is determined in the heart rate graph and used as the initial curve peak information corresponding to the initial heart rate data, interpolation processing is conveniently carried out on the basis of the initial curve peak information, and therefore the heart rate data with low sampling rate is updated into the simulated heart rate data with high sampling rate, and calculation of the heart rate variability at the current moment is facilitated.

Further, when determining the peak information of the initial curve, considering that the initial heart rate data is an electrocardiosignal acquired by a sensor, the initial heart rate data needs to be processed into the curve and then can be used for analyzing heart rate variability; in this embodiment, the implementation is as in step S2022 to step S2024.

Step S2022, preprocessing the initial heart rate data, and determining curve information corresponding to the initial heart rate data according to the preprocessing result.

Specifically, preprocessing refers to processing of the initial heart rate data into a process that can be used for heart rate variability calculations, including but not limited to segmentation, filtering, normalization processes. Correspondingly, the curve information specifically refers to recording all information corresponding to a heart rate change curve generated according to the initial heart rate data, including, but not limited to, peak positions, valley positions, curve shapes, values of curve peaks, values of valleys and the like in the curve.

Based on the method, in order to construct high-frequency target heart rate data on the basis of low-frequency initial heart rate data, the initial heart rate data can be preprocessed to be processed into standard heart rate data which can be used for subsequent use, and a heart rate change curve is drawn on the basis of the standard heart rate data, so that curve information corresponding to the initial heart rate data is determined, and subsequent determination of a peak interval sequence on the basis of the curve information is facilitated to be used for calculating heart rate variability.

Further, when determining the curve information of the initial heart rate data, the initial heart rate data not only includes usable heart rate data but also includes some noise heart rate data, so that in order to improve the calculation accuracy of the heart rate variability, the initial heart rate data can be preprocessed; in this embodiment, the specific implementation manner is as follows:

The initial heart rate data are subjected to segmentation processing according to a first set duration to obtain a plurality of initial heart rate data segments, wherein any two adjacent initial heart rate data segments in the plurality of initial heart rate data segments have overlapping intervals corresponding to a second set duration, and the second set duration is smaller than the first set duration; carrying out band-pass filtering on each initial heart rate data segment, and carrying out normalization processing on each initial heart rate data segment after the band-pass filtering; determining the sub-curve information of each initial heart rate data segment according to the normalization processing result, and determining the curve information corresponding to the initial heart rate data based on the sub-curve information of each initial heart rate data segment.

Specifically, the segmentation process specifically refers to an operation of dividing the initial heart rate data into a plurality of initial heart rate data segments with a first set duration, where the first set duration may be set according to actual requirements, for example, 5s,6s, 8s, etc., and the embodiment is not limited in any way herein. Accordingly, the second set duration specifically refers to a duration of an overlapping region of any two adjacent initial heart rate data segments in the plurality of initial heart rate data segments, where the second set duration is smaller than the first set duration, for example, 1s,1.5s, or 2s, and the embodiment is not limited in any way. Correspondingly, the sub-curve information specifically refers to curve information corresponding to each initial heart rate data segment, and the curve information records all information of a change curve corresponding to the initial heart rate data segment, including, but not limited to, peak positions, valley positions, curve shapes, values of curve peaks, values of valleys and the like in a local curve corresponding to the initial heart rate data segment. The bandpass filtering may be performed by using a predetermined bandpass filter, such as a butterworth bandpass filter.

Based on the above, after the initial heart rate data corresponding to the target user is obtained, in order to facilitate the subsequent use, the initial heart rate data may be first subjected to segmentation processing according to a first set duration, so as to obtain a plurality of initial heart rate data segments according to a segmentation processing result; in the segmentation process, in order to avoid data loss and influence the relevance between adjacent data segments, the segmentation can be performed according to the adjacent heart rate data segments by reserving an overlapping area with a second set duration, and the second set duration is smaller than the first set duration, so that a plurality of initial heart rate data segments meeting the follow-up processing are obtained.

On the basis, in order to obtain curve information corresponding to the initial heart rate data, the method is used for determining initial curve crest information, band-pass filtering can be carried out on each initial heart rate data segment, and normalization processing can be carried out on each band-pass filtered initial heart rate data segment; determining the sub-curve information of each initial heart rate data segment according to the normalization processing result, and finally integrating the sub-curve information of each initial heart rate data segment to determine the curve information corresponding to the initial heart rate data.

For example, under the circumstance that the bracelet worn by the user first needs to calculate the heart rate variability of the user first, the PPG signal is acquired by the photoelectric sensor for the user first, after the PPG signal with the sampling rate of fs=25 Hz is acquired in real time, the PPG signal can be subjected to segmentation processing according to the fixed duration t1=5s, and n data segments are obtained according to the segmentation processing result. It should be noted that, in the segmentation process, the nth data segment needs to be combined with a data interval with a fixed time length t2=1s after the nth-1 data segment to construct a data segment containing (t1+t2) fs sampling points, that is, adjacent data segments have t2 fs overlapping sampling points; for example, the nth data segment corresponds to 0-5s, which includes 0-150 sampling points, the n+1th data segment corresponds to 4-9s, which includes 125-275 sampling points, wherein the last 25 sampling points in the nth data segment overlap the first 25 sampling points in the n+1th data segment, and the n data segment corresponding to the PPG signal is constructed based on the last 25 sampling points.

Further, after n data segments are obtained, a third-order butterworth band-pass filter can be used for carrying out band-pass filtering on each data segment, normalization processing is carried out on the n data segments after the band-pass filtering, sub-curve information corresponding to each data segment is obtained at the moment, curve information corresponding to an original PPG signal is constructed on the basis of the sub-curve information, the subsequent generation of a PPG signal with a high sampling rate by using a PPG signal with a low sampling rate is facilitated, and the PPG signal with the high sampling rate is used for obtaining RR interval sequences with high time resolution, so that the calculation of HRV is completed.

In conclusion, through preprocessing initial heart rate data, the initial heart rate data can be converted into a heart rate change curve, curve information is obtained based on the heart rate change curve, and the subsequent determination of initial curve peak information according to the curve information is facilitated, so that heart rate variability is calculated rapidly and accurately.

Step S2024 generates initial curve peak information corresponding to the initial heart rate data based on the curve information.

Specifically, after the curve information corresponding to the initial heart rate data is obtained, further, the curve information can be analyzed by considering that the curve information contains all the information of the heartbeat change curve corresponding to the initial heart rate data, so that the initial curve peak information is obtained, and the subsequent use is convenient.

Furthermore, when determining the peak information of the initial curve, since the expression forms of the heartbeat rule in the curve are different, and the different expression forms represent different heartbeat rules, part of the heartbeat rules are actually noise heartbeats, and therefore screening is needed; in this embodiment, the specific implementation manner is as follows:

calculating curve characteristic information corresponding to the initial heart rate data according to the curve information, wherein the curve characteristic information comprises a characteristic value of each curve peak in a plurality of curve peaks; comparing the characteristic value of each curve peak with a characteristic threshold value, and determining an initial curve peak in the plurality of curve peaks according to a comparison result; and determining initial curve crest information corresponding to the initial heart rate data based on the initial curve crest.

Specifically, the curve characteristic information specifically refers to a set formed by characteristic values corresponding to each curve peak obtained through calculation according to information such as peak position, trough position, curve shape, value of the curve peak, value of the trough and the like contained in the curve information, wherein the characteristic values include, but are not limited to, width, peak height, trend and the like of each curve peak and are used for realizing comparison with a characteristic threshold value, so that peaks which cannot represent heartbeats are screened out and removed, the rest peaks are used as initial curve peaks corresponding to initial heart rate data, information of the curve peaks is conveniently determined subsequently, and the information is used for realizing conversion of low-sampling-rate initial heart rate data into high-sampling-rate target heart rate data. The feature threshold may be set separately for different types of feature values, which is not limited in this embodiment.

Based on the above, after the curve information corresponding to the initial heart rate data is obtained, the characteristic value of each curve peak in the heart rate variation curve can be calculated according to the information such as the peak position, the trough position, the curve shape, the value of the curve peak, the value of the trough and the like recorded in the curve information, and the characteristic information of the curve can be formed. At this time, considering that not all peaks can represent the beating condition of the heartbeat, the abnormal curve peaks need to be removed, so that the initial curve peaks are reserved; the characteristic value of each curve peak can be compared with a characteristic threshold value, and an initial curve peak is determined from a plurality of curve peaks according to the comparison result; and finally, determining initial curve crest information corresponding to the initial heart rate data based on the initial curve crest.

After obtaining the curve information of the heartbeat variation curve corresponding to the original PPG signal, the peak searching algorithm can be adopted to find out the curve peak positions of each data segment in the n data segments, at the moment, the adjacent trough positions of the curve peaks can be determined according to the peak positions, the peak width and the normalized peak height of each curve peak are calculated by combining the peak position and the adjacent trough position of each curve peak, and then the peak width and the normalized peak height of each curve peak are compared with the set peak width threshold w _th (e.g. data length of 0.1s, i.e. fs 0.1s sampling points) and setting a normalized peak height threshold h _th And (e.g. 0.1), eliminating curve peaks smaller than a threshold according to the comparison result, eliminating peaks which cannot represent heart beat, and taking the rest curve peaks as curve peaks of the original PPG signal, so that the HRV can be conveniently calculated and used subsequently.

In sum, the curve wave crest which cannot represent the heart beat is removed by calculating the characteristic value, so that the influence of the curve wave crest on the subsequent calculation can be avoided, and the calculation accuracy of the heart rate variability is further improved.

Step S204, a data window is created for the initial curve crest information, interpolation processing is carried out on the data window, and target heart rate data is generated according to the processing result.

Specifically, after the initial curve peak information corresponding to the initial heart rate data is obtained, further, in order to calculate the heart rate variability of the target user based on the electrocardiosignal with a low sampling rate, the initial heart rate data with the low sampling rate can be updated in an interpolation mode, so that the initial heart rate data with the low sampling rate can be converted into the target heart rate data with the high sampling rate, and the peak interval parameter with the high time resolution can be conveniently obtained on the basis of the initial heart rate data, so that the heart rate variability of the target user can be accurately calculated. In the conversion process, in order to ensure reasonable interpolation positions, interpolation can be performed by taking an initial curve peak of initial heart rate data as an anchor point, namely, the position of the initial curve peak is determined according to initial curve peak information, then a data window is constructed on the basis of the position point, interpolation processing is performed on the data window, and therefore interpolation can be performed on all initial curve peaks in the initial heart rate data, and therefore target heart rate data with high sampling rate can be obtained through interpolation results.

It should be noted that, when interpolation processing is performed, it is required to ensure that the interpolated heart rate data can satisfy calculation of heart rate variability, for example, the sampling rate of the initial heart rate data is 25Hz, and the sampling rate of the calculated heart rate variability needs to reach 1000Hz, and when interpolation processing is performed, the initial heart rate data needs to be lifted from 25Hz to 1000Hz, so as to facilitate subsequent use.

The data window specifically refers to a window formed by selecting data with sampling points set adjacently before and after using an initial curve peak corresponding to the initial curve peak information as an anchor point, where the size of the window can be set according to actual requirements, and the embodiment is not limited in any way. Accordingly, a polynomial interpolation algorithm is adopted in the interpolation processing, and lagrangian interpolation, newton interpolation and the like can be selected, and in addition, a linear interpolation and the like method can be used according to specific needs, and the embodiment is not limited in any way. The target heart rate data specifically refers to heart rate data with high sampling rate obtained after interpolation processing, and meets the calculation requirement of subsequent heart rate variability.

That is, by selecting a data window that may contain a peak value to perform interpolation, it is possible to ensure that target heart rate data with a high sampling rate is obtained, and based on this, a peak-to-peak interval sequence with a high time resolution can be obtained, so that the sampling rate independent of the initial heart rate data itself is realized, thereby avoiding increasing the power consumption of the client.

Furthermore, in the process of generating the target heart rate data based on the initial heart rate data, the process of interpolating is based on the data window, and the construction of the data window and the selection of the interpolation algorithm are the bases for determining the accuracy of the target heart rate data, so the method can be realized as follows:

determining an initial peak point corresponding to the initial heart rate data according to the initial curve peak information; selecting a set number of front adjacent sampling points and rear adjacent sampling points which are adjacent to the initial peak point in the initial heart rate data; creating the data window according to the initial peak sampling point, the front adjacent sampling point and the rear adjacent sampling point; and processing the data window according to a preset interpolation algorithm, and generating the target heart rate data according to a processing result.

Specifically, the initial peak point specifically refers to a position point of an initial curve peak corresponding to the initial curve peak information, and correspondingly, the front adjacent sampling point specifically refers to a sampling point adjacent to the initial peak point in the forward direction, the rear adjacent sampling point specifically refers to a sampling point adjacent to the initial peak point in the backward direction, and the number of the front adjacent sampling point and the rear adjacent sampling point is the same, and is set, and the set number can be set according to actual requirements. Correspondingly, the interpolation algorithm is an algorithm capable of carrying out interpolation processing based on a data window and is used for inserting a new sampling point between any two sampling points and converting initial heart rate data with a low sampling rate into target heart rate data with a high sampling rate.

Based on the initial curve peak information, after the initial curve peak information corresponding to the initial heart rate data is obtained, an initial peak point corresponding to the initial heart rate data can be determined according to the initial curve peak information; then, based on the initial peak point, selecting a front adjacent sampling point and a rear adjacent sampling point which are adjacent to the initial peak point and are of set quantity in initial heart rate data; creating a data window corresponding to each initial curve peak according to the initial peak sampling point, the front adjacent sampling point and the rear adjacent sampling point; at this time, the data window corresponding to each initial curve peak is processed according to a preset interpolation algorithm, so that a target data window corresponding to each initial curve peak can be obtained, and then the target data windows corresponding to each initial curve peak are spliced, so that the amplification of sampling points can be realized, and the target heart rate data is generated according to the processing result, so that the conversion of the initial heart rate data with a low sampling rate into the target heart rate data with a high sampling rate is realized, the peak-to-peak interval parameter with a high time resolution is conveniently obtained on the basis of the target heart rate data, and the heart rate variability of a target user is accurately calculated.

Along the above example, after the curve peak of the original PPG signal is screened out, m1 (e.g. 10) and m2 (e.g. 10) sampling points can be selected forward and backward respectively according to the peak position point of each curve peak, so as to obtain a data window corresponding to the peak position point of each curve peak, and each data window contains (m1+1+m2) sampling points; and then, a preset interpolation algorithm can be adopted to perform polynomial interpolation on each data window, so that the original PPG signal with a lower Fs sampling rate is improved to a target PPG signal with a higher Fs sampling rate (such as 1000 Hz), and the subsequent calculation of heart rate variability based on the original PPG signal is facilitated.

In conclusion, the sampling points are amplified by adopting a positioning interpolation mode, so that the initial heart rate data with low sampling rate can be converted into the target heart rate data with high sampling rate, the peak-to-peak interval parameter with high time resolution can be conveniently obtained on the basis, and the heart rate variability of a target user can be accurately calculated.

Step S206, determining target curve crest information corresponding to the target heart rate data, and constructing a peak-to-peak interval sequence according to the target curve crest information.

Specifically, after the target heart rate data with a higher sampling rate is obtained, the target heart rate data can be used as a basis for calculating heart rate variability, and calculation of heart rate variability is needed to be completed according to peak intervals in a heart rate change curve, namely RR intervals are needed to be realized, so that peak interval sequences are needed to be obtained first, at the moment, target curve peak information corresponding to the target heart rate data can be determined first and used for determining peak positions corresponding to the target heart rate data, and then the peak interval sequences are constructed according to the target curve peak information, so that subsequent use is facilitated.

The target curve peak information is specifically the position information of each curve peak in a heart rate change curve constructed based on target heart rate data, and is used for representing information such as a heart beat rule of a user. Correspondingly, the peak-to-peak interval sequence specifically refers to a sequence composed of curve peak interval parameters, namely an RR interval sequence, in a heart rate variation curve corresponding to target heart rate data, and is used for calculating heart rate variability.

Further, after determining the peak information of the target curve, considering that the peak-to-peak interval parameter is the basis for calculating the heart rate variability, determining a sequence consisting of the peak-to-peak interval parameter by combining the peak information of the target curve corresponding to the target heart rate data; in this embodiment, the specific implementation manner is as follows:

acquiring a target data window corresponding to the target curve crest information and a target data segment to which the target curve crest information belongs; determining first relative position information of the target curve peak information with respect to the target heart rate data and second relative position information of the target data window with respect to the target data segment; calculating absolute position information of the target curve crest information in the target heart rate data according to the first relative position information and the second relative position information; the peak-to-peak interval sequence is constructed based on the absolute position information.

Specifically, the target data window is a data window obtained by interpolation processing of the data window by the pointer; correspondingly, the target data segment specifically refers to a data segment to which each peak position point belongs; correspondingly, the first relative position information specifically refers to a data segment to which the peak position point selected at the current moment belongs, and the relative position in the target heart rate data, and the second relative position information specifically refers to a target data window to which the peak position point selected at the current moment belongs, and the relative position in the target data segment to which the peak position point selected at the current moment belongs. Correspondingly, the absolute position information specifically refers to the absolute position of the peak position point selected at the current moment in the target heart rate data, and is used for facilitating subsequent calculation of the peak-to-peak interval sequence.

Based on the above, when the peak-to-peak interval sequence needs to be constructed, a target data window corresponding to the peak information of the target curve and a target data segment to which the target data window belongs can be acquired first; at this time, first relative position information of the target curve peak information relative to the target heart rate data and second relative position information of the target data window relative to the target data segment can be determined first; and then, according to the first relative position information and the second relative position information, the absolute position information of the target curve peak information in the target heart rate data can be calculated, and after the absolute position information of each curve peak position point is obtained based on the absolute position information, a peak-to-peak interval sequence can be constructed based on the absolute position information, so that the follow-up use is convenient.

According to the above example, after the target PPG signal is obtained, a new peak position can be found in the target PPG signal, then the target PPG signal recording starting time is taken as 0 time, the relative position of the nth data segment in the target PPG signal is determined, the relative position of the interpolated data window corresponding to the peak position point in the nth data segment is determined, then the absolute position of the nth data Duan Zhongbo peak position point in the target PPG signal is calculated by combining the two relative positions to record, and the absolute position has a time resolution of 1/Fs; after the absolute positions of the peak position points corresponding to the n data segments in the target PPG signal are recorded, the peak position points can be used for constructing RR interval sequences, and the subsequent calculation of heart rate variability is facilitated.

In summary, by combining the first relative position information and the second relative position information, absolute position information of all curve peak position points can be mined from target heart rate data, and peak-to-peak interval sequences are constructed based on the absolute position information, so that calculation accuracy of heart rate variability can be ensured.

Furthermore, after absolute position information is obtained by calculation, the position of each wave crest in the target heart rate data can be determined, but the possibility of overlapping of adjacent data segments is considered, and the calculation accuracy of heart rate variability can be influenced by constructing a sequence based on the possibility, so that screening can be performed; in this embodiment, the specific implementation manner is as follows:

constructing an absolute position sequence according to the absolute position information; updating the absolute position sequence based on the overlapping position information to obtain a target absolute position sequence when the overlapping position information is contained in the absolute position sequence; the peak-to-peak interval sequence is obtained by performing differential processing on target absolute position information contained in the target absolute position sequence.

Specifically, the absolute position sequence specifically means a sequence composed according to absolute position information for performing differential processing to obtain a peak-to-peak interval sequence. Accordingly, the overlapping position information refers to the repeated occurrence of absolute position information, which may affect the calculation of heart rate variability, and thus needs to be removed. The target absolute position sequence specifically refers to a sequence composed according to the absence of overlapping absolute position information.

Based on this, after determining the target absolute position information, an absolute position sequence can be constructed from the absolute position information; if the overlapping position information does not exist, the peak interval sequence can be directly constructed, and if the overlapping position information exists in the absolute position sequence, the existence of the overlapping absolute position information is indicated, so that the absolute position sequence can be updated based on the overlapping position information and used for eliminating the overlapping absolute position information, and the target absolute position sequence is obtained; and finally, carrying out differential processing on target absolute position information contained in the target absolute position sequence to obtain a peak-to-peak interval sequence.

In the above example, since adjacent data segments have overlapping portions, it is necessary to check whether there is a case where a repeated value occurs due to the overlapping segments in the obtained absolute position sequence, and if so, delete the repeated absolute position sequence value; and by analogy, after all absolute position sequence values are traversed and repeated absolute position sequence values are removed, an unprocessed peak absolute position sequence can be obtained, and an unprocessed RR interval sequence can be obtained by carrying out differential processing on the peak absolute position sequence values contained in the sequence so as to be used for calculating the subsequent heart rate variability.

In conclusion, by eliminating repeated absolute position information, influence of the overlapped absolute position information on the construction accuracy of the peak interval sequence can be avoided, so that the heart rate variability with higher accuracy can be conveniently calculated later.

Step S208, updating the peak interval sequence to a target peak interval sequence, and calculating the heart rate variability of the target user by using the peak interval parameters contained in the target peak interval sequence.

Specifically, after the peak-to-peak interval sequence is obtained, the peak-to-peak interval sequence is updated to the target peak-to-peak interval sequence, so that the abnormal peak-to-peak interval parameter is corrected, the target peak-to-peak interval sequence is obtained, and the heart rate variability of the target user can be calculated based on the target peak-to-peak interval sequence, considering that the peak-to-peak interval parameter contained in the peak-to-peak interval sequence is obtained by combining the target heart rate data, and the target heart rate data is obtained by converting the initial heart rate data based on the low sampling rate.

The target peak-to-peak interval sequence specifically refers to a sequence composed of non-abnormal curve peak interval parameters in a heart rate change curve corresponding to target heart rate data, namely an RR interval sequence, and is used in heart rate variability calculation.

Further, in the process of updating from the peak-to-peak interval sequence to the target peak-to-peak interval sequence, in order to avoid the influence of the abnormal peak-to-peak interval parameter on heart rate variability calculation, the sequence may be updated according to the abnormal peak-to-peak interval parameter; in this embodiment, the specific implementation manner is as follows:

traversing the initial peak interval parameters contained in the peak interval sequence, and determining abnormal peak interval parameters according to the traversing result; updating the peak-to-peak interval sequence based on the abnormal peak-to-peak interval parameter to obtain the target peak-to-peak interval sequence.

Specifically, the abnormal peak interval parameter specifically refers to a peak interval parameter in the peak interval sequence, which does not meet a preset condition and affects the calculation of heart rate variability, namely an abnormal RR interval sequence value; correspondingly, updating the peak-to-peak interval sequence based on the abnormal peak-to-peak interval parameter, specifically, correcting the abnormal peak-to-peak interval parameter, so as to obtain a target peak-to-peak interval sequence which does not contain the abnormal peak-to-peak interval parameter.

Based on the above, after obtaining the peak-to-peak interval sequence based on the target heart rate data, the initial peak-to-peak interval parameter contained in the peak-to-peak interval sequence may be traversed first, so as to determine the abnormal peak-to-peak interval parameter according to the traversing result; and updating the peak-to-peak interval sequence based on the abnormal peak-to-peak interval parameter to correct the abnormal peak-to-peak interval parameter, thereby obtaining the target peak-to-peak interval sequence.

In practical application, when the correction processing is performed, the abnormal peak interval parameter may be updated according to the theoretical parameter, or the abnormal peak interval parameter may be removed, or the abnormal peak interval parameter may be updated according to the adjacent normal peak interval parameter, and in specific implementation, the correction processing may be selected according to the actual requirement, and the embodiment is not limited in any way.

In summary, by updating the peak-to-peak interval sequence according to the abnormal peak-to-peak interval parameter, a target peak-to-peak interval sequence without abnormal information can be obtained, and the heart rate variability is calculated based on the target peak-to-peak interval sequence without abnormal information, so that the calculation accuracy can be ensured.

Further, in updating, the degree of abnormality of the abnormal peak interval parameter is considered to be possibly higher or lower, so that different adjustments are required for different abnormal peak interval parameters; in this embodiment, the specific implementation manner is as follows:

(1) Determining a first correction threshold and a second correction threshold according to a preset correction strategy; determining a first peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter in the peak-to-peak interval sequence if the abnormal peak-to-peak interval parameter is greater than the first correction threshold and the second correction threshold; screening associated peak-to-peak interval parameters from the peak-to-peak interval sequence according to the abnormal peak-to-peak interval parameters and the first peak-to-peak interval parameters; updating the abnormal peak interval parameter and the associated peak interval parameter based on the second peak interval parameter and the first peak interval parameter associated with the abnormal peak interval parameter, and generating the target peak interval sequence according to an updating result.

Specifically, the correction strategy specifically refers to a strategy for correcting the abnormal peak interval parameter, the first correction threshold specifically refers to a threshold value obtained by calculating the peak interval parameter adjacent to the peak interval parameter processed at the current moment according to a preset percentage threshold value, and the second correction threshold specifically refers to an upper limit threshold value of the peak interval parameter, and the peak interval parameter with excessively high abnormality can be determined through the first correction threshold value and the second correction threshold value. Correspondingly, the first peak-to-peak interval parameter specifically refers to a first normal peak-to-peak interval parameter after the abnormal peak-to-peak interval parameter; correspondingly, the associated peak-to-peak interval parameter specifically refers to all abnormal peak-to-peak interval parameters between the abnormal peak-to-peak interval parameter and the first peak-to-peak interval parameter; accordingly, the second peak-to-peak spacing parameter is specifically the overall normal peak-to-peak spacing parameter preceding the abnormal peak-to-peak spacing parameter. The updating is the operation of updating the abnormal peak-to-peak interval parameter by using the normal peak-to-peak interval parameter.

Based on the above, when the target peak-to-peak interval sequence is constructed, a first correction threshold and a second correction threshold can be determined according to a preset correction strategy; when the abnormal peak interval parameter is greater than the first correction threshold and the second correction threshold, the peak interval parameter at the current moment is abnormal, and in order to correct the abnormal peak interval parameter, the first peak interval parameter related to the abnormal peak interval parameter can be determined in the peak interval sequence; screening all abnormal peak interval parameters between the abnormal peak interval parameters and the first peak interval parameters in the peak interval sequence according to the abnormal peak interval parameters and the first peak interval parameters, and taking the abnormal peak interval parameters as associated peak interval parameters; at this time, updating the abnormal peak interval parameter and the associated peak interval parameter based on the second peak interval parameter and the first peak interval parameter associated with the abnormal peak interval parameter, so as to correct the abnormal peak interval parameter and generate a target peak interval sequence according to the updating result.

For example, after the above-mentioned RR interval sequence corresponding to the target PPG signal is obtained, the reasonable upper threshold u of RR interval value can be set considering that the target PPG signal records the mean value and standard deviation of heart rate in time _th (e.g., 300 ms) and a lower threshold d _th (e.g., 2000 ms), and select a certain percentage threshold p _th (0<p _th <1, such as 0.5). Based on this, the nth RR interval value is selected in the RR interval sequence as rri _n . It should be noted that rri ₁ To rri _n-1 Are all processed normal RR interval values.

Further, if rri _n >(1+p _th )*rri _n-1 And rri is _n >u _th Indicating that the currently selected RR interval value is abnormal with a large bias, which cannot be used to calculate heart rate variability, and thus can be obtained from rri _n Start looking back one by one until rri is found _m Satisfy condition d _th <rri _m <u _th ，(m-1>n), then from rri _n To rri _m-1 All are abnormal RR interval values, at the moment, m-n abnormal RR interval values can be determined to exist, and the abnormal RR interval values are recorded as sun; at this time, it is determined that the abnormal data segment should contain RR interval values of num=round (sum×2/(rri) _n-1 +rri _m ) Round represents rounding off the values in brackets (e.g., 2.4 is 2, 2.5 is 3).

Further, to be able to correct all abnormal RR interval values in the abnormal data segment, the data segment can be modified according to rri _n-1 、rri _n-1 Previous RR interval value and rri _m At rri _n-1 And rri _m Inserting num theoretical RR interval values, and ensuring that the sum of the inserted theoretical RR interval values is equal to sum; therefore, the abnormal RR interval value between n and m is corrected, and the abnormal RR interval value with larger amplitude is corrected, so that heart rate variability is calculated conveniently on the basis of the abnormal RR interval value.

In summary, when correcting the abnormal peak-to-peak interval parameter with a larger size, the correct peak-to-peak interval parameter can be adopted for replacement, so that the obtained target peak-to-peak interval sequence is ensured to be the normal peak-to-peak interval parameter, and the calculation accuracy of heart rate variability is effectively improved.

(2) Determining a third correction threshold and a fourth correction threshold according to a preset correction strategy; determining a third peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter in the peak-to-peak interval sequence if the abnormal peak-to-peak interval parameter is less than the third correction threshold and the fourth correction threshold; calculating an updated peak-to-peak interval parameter based on the abnormal peak-to-peak interval parameter and the third peak-to-peak interval parameter; and under the condition that the updated peak-to-peak interval parameter meets a preset parameter threshold, updating the abnormal peak-to-peak interval parameter by using the updated peak-to-peak interval parameter, and generating the target peak-to-peak interval sequence according to an updating result.

Specifically, the third correction threshold is specifically a threshold calculated according to a preset percentage threshold and an adjacent peak-to-peak interval parameter before the peak-to-peak interval parameter processed at the current moment, and correspondingly, the fourth correction threshold is specifically a lower limit threshold of the peak-to-peak interval parameter. The peak-to-peak interval parameter of the excessively low abnormality may be determined by the third correction threshold and the fourth correction threshold. Correspondingly, the third peak-to-peak interval parameter specifically refers to the peak-to-peak interval parameter adjacent to the abnormal peak-to-peak interval parameter; correspondingly, updating the peak-to-peak interval parameter specifically refers to combining the abnormal peak-to-peak interval parameter with the third peak-to-peak interval parameter to obtain a new peak-to-peak interval parameter.

Based on the above, when the target peak-to-peak interval sequence is constructed, a third correction threshold and a fourth correction threshold can be determined according to a preset correction strategy; when the abnormal peak interval parameter is smaller than the third correction threshold and the fourth correction threshold, the peak interval parameter at the current moment is abnormal, and in order to correct the abnormal peak interval parameter, the third peak interval parameter related to the abnormal peak interval parameter can be determined in the peak interval sequence; calculating an updated peak-to-peak interval parameter based on the abnormal peak-to-peak interval parameter and the third peak-to-peak interval parameter; the calculated updated peak-to-peak interval parameter may or may not satisfy the condition, and under the condition that the updated peak-to-peak interval parameter satisfies the preset parameter threshold, the updated peak-to-peak interval parameter may be used to update the abnormal peak-to-peak interval parameter, so as to modify the excessively low peak-to-peak interval parameter, and generate the target peak-to-peak interval sequence according to the update result.

When the updated peak-to-peak interval parameter does not meet the preset parameter threshold, the method indicates that the method cannot be used, and then the correction is required to be continued, and a fourth peak-to-peak interval parameter related to the abnormal peak-to-peak interval parameter can be determined in the peak-to-peak interval sequence; taking the updated peak-to-peak interval parameter as an abnormal peak-to-peak interval parameter and the fourth peak-to-peak interval parameter as a third peak-to-peak interval parameter, and executing a step of calculating the updated peak-to-peak interval parameter based on the abnormal peak-to-peak interval parameter and the third peak-to-peak interval parameter; and executing the step of updating the abnormal peak-to-peak interval parameter by using the updated peak-to-peak interval parameter until the updated peak-to-peak interval parameter meets the preset parameter threshold value, and generating the target peak-to-peak interval sequence according to an updating result.

In the above example, if rri _n <(1-p _th )*rri _n-1 And rri is _n <d _th Indicating that the currently selected RR interval value is abnormal with small magnitude, which cannot be used to calculate heart rate variability, rri can be used _n And rri _n+1 Adding to obtain a new rri _n And repeating the new obtained rri _n Make a judgment while the original rri _n+2 Becomes a new rri _n+1 And so on until the accumulated rri _n Neither abnormally large nor abnormally small, can be used as a normal RR interval value. Furthermore, when all of the n RR interval values are compared and no abnormal RR interval value exists, a reliable RR interval sequence for HRV analysis can be obtained, and HRV calculation can be performed based on the reliable RR interval sequence.

The application of the data processing method provided in the present specification in a sports scene is taken as an example in the following description with reference to fig. 3, and the data processing method is further described. Fig. 3 shows a process flow chart of a data processing method according to an embodiment of the present disclosure, which specifically includes the following steps:

step S302, initial heart rate data corresponding to a target user are obtained, preprocessing is carried out on the initial heart rate data, and curve information corresponding to the initial heart rate data is determined according to a preprocessing result.

Specifically, the initial heart rate data are subjected to segmentation processing according to a first set duration to obtain a plurality of initial heart rate data segments, wherein any two adjacent initial heart rate data segments in the plurality of initial heart rate data segments have overlapping intervals corresponding to a second set duration, and the second set duration is smaller than the first set duration; carrying out band-pass filtering on each initial heart rate data segment, and carrying out normalization processing on each initial heart rate data segment after the band-pass filtering; determining the sub-curve information of each initial heart rate data segment according to the normalization processing result, and determining the curve information corresponding to the initial heart rate data based on the sub-curve information of each initial heart rate data segment.

Step S304, generating initial curve crest information corresponding to the initial heart rate data based on the curve information.

Specifically, calculating curve characteristic information corresponding to the initial heart rate data according to the curve information, wherein the curve characteristic information comprises a characteristic value of each curve peak in a plurality of curve peaks; comparing the characteristic value of each curve peak with a characteristic threshold value, and determining an initial curve peak in the plurality of curve peaks according to a comparison result; and determining initial curve crest information corresponding to the initial heart rate data based on the initial curve crest.

Step S306, determining an initial peak point corresponding to the initial heart rate data according to the initial curve peak information.

In step S308, in the initial heart rate data, a set number of front adjacent sampling points and rear adjacent sampling points adjacent to the initial peak point are selected.

Step S310, a data window is created according to the initial peak sampling point, the front adjacent sampling point and the rear adjacent sampling point.

Step S312, processing the data window according to a preset interpolation algorithm, and generating target heart rate data according to the processing result.

Step S314, determining target curve peak information corresponding to the target heart rate data, and acquiring a target data window corresponding to the target curve peak information and a target data segment to which the target data window belongs.

In step S316, first relative position information of the target curve peak information with respect to the target heart rate data and second relative position information of the target data window with respect to the target data segment are determined.

Step S318, calculating absolute position information of the target curve crest information in the target heart rate data according to the first relative position information and the second relative position information.

Step S320, a peak-to-peak interval sequence is constructed based on the absolute position information.

Specifically, an absolute position sequence is constructed according to the absolute position information; updating the absolute position sequence based on the overlapping position information to obtain a target absolute position sequence when the overlapping position information is contained in the absolute position sequence; the peak-to-peak interval sequence is obtained by performing differential processing on target absolute position information contained in the target absolute position sequence.

Step S322, traversing the initial peak interval parameters contained in the peak interval sequence, and determining abnormal peak interval parameters according to the traversing result.

Step S324, updating the peak-to-peak interval sequence based on the abnormal peak-to-peak interval parameter to obtain the target peak-to-peak interval sequence.

On the one hand, a first correction threshold value and a second correction threshold value are determined according to a preset correction strategy; determining a first peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter in the peak-to-peak interval sequence if the abnormal peak-to-peak interval parameter is greater than the first correction threshold and the second correction threshold; screening associated peak-to-peak interval parameters from the peak-to-peak interval sequence according to the abnormal peak-to-peak interval parameters and the first peak-to-peak interval parameters; updating the abnormal peak interval parameter and the associated peak interval parameter based on the second peak interval parameter and the first peak interval parameter associated with the abnormal peak interval parameter, and generating the target peak interval sequence according to an updating result.

On the other hand, determining a third correction threshold and a fourth correction threshold according to a preset correction strategy; determining a third peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter in the peak-to-peak interval sequence if the abnormal peak-to-peak interval parameter is less than the third correction threshold and the fourth correction threshold; calculating an updated peak-to-peak interval parameter based on the abnormal peak-to-peak interval parameter and the third peak-to-peak interval parameter; and under the condition that the updated peak-to-peak interval parameter meets a preset parameter threshold, updating the abnormal peak-to-peak interval parameter by using the updated peak-to-peak interval parameter, and generating the target peak-to-peak interval sequence according to an updating result.

Wherein, in case the updated peak-to-peak interval parameter does not meet a preset parameter threshold, a fourth peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter is determined in the peak-to-peak interval sequence; taking the updated peak-to-peak interval parameter as the abnormal peak-to-peak interval parameter and the fourth peak-to-peak interval parameter as the third peak-to-peak interval parameter, and performing a step of calculating an updated peak-to-peak interval parameter based on the abnormal peak-to-peak interval parameter and the third peak-to-peak interval parameter; and executing the step of updating the abnormal peak-to-peak interval parameter by using the updated peak-to-peak interval parameter until the updated peak-to-peak interval parameter meets the preset parameter threshold value, and generating the target peak-to-peak interval sequence according to an updating result.

Step S326, calculating the heart rate variability of the target user by using the peak-to-peak interval parameters contained in the target peak-to-peak interval sequence.

Corresponding to the method embodiment described above, the present disclosure further provides an embodiment of a data processing apparatus, and fig. 4 shows a schematic structural diagram of a data processing apparatus according to an embodiment of the present disclosure. As shown in fig. 4, the apparatus is applied to a client, and includes:

an acquisition module 402, configured to acquire initial heart rate data corresponding to a target user, and determine initial curve peak information corresponding to the initial heart rate data;

the creating module 404 is configured to create a data window for the initial curve crest information, perform interpolation processing on the data window, and generate target heart rate data according to a processing result;

a determining module 406, configured to determine target curve peak information corresponding to the target heart rate data, and construct a peak-to-peak interval sequence according to the target curve peak information;

a calculation module 408 configured to update the peak-to-peak interval sequence to a target peak-to-peak interval sequence and calculate a heart rate variability of the target user using peak-to-peak interval parameters contained in the target peak-to-peak interval sequence.

In an alternative embodiment, the acquisition module 402 is further configured to:

preprocessing the initial heart rate data, and determining curve information corresponding to the initial heart rate data according to a preprocessing result; and generating initial curve crest information corresponding to the initial heart rate data based on the curve information.

In an alternative embodiment, the creation module 404 is further configured to:

In an alternative embodiment, the determining module 406 is further configured to:

In an alternative embodiment, the computing module 408 is further configured to:

determining a first correction threshold and a second correction threshold according to a preset correction strategy; determining a first peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter in the peak-to-peak interval sequence if the abnormal peak-to-peak interval parameter is greater than the first correction threshold and the second correction threshold; screening associated peak-to-peak interval parameters from the peak-to-peak interval sequence according to the abnormal peak-to-peak interval parameters and the first peak-to-peak interval parameters; updating the abnormal peak interval parameter and the associated peak interval parameter based on the second peak interval parameter and the first peak interval parameter associated with the abnormal peak interval parameter, and generating the target peak interval sequence according to an updating result.

determining a third correction threshold and a fourth correction threshold according to a preset correction strategy; determining a third peak-to-peak interval parameter associated with the abnormal peak-to-peak interval parameter in the peak-to-peak interval sequence if the abnormal peak-to-peak interval parameter is less than the third correction threshold and the fourth correction threshold; calculating an updated peak-to-peak interval parameter based on the abnormal peak-to-peak interval parameter and the third peak-to-peak interval parameter; and under the condition that the updated peak-to-peak interval parameter meets a preset parameter threshold, updating the abnormal peak-to-peak interval parameter by using the updated peak-to-peak interval parameter, and generating the target peak-to-peak interval sequence according to an updating result.

According to the data processing device provided by the specification, in order to improve the determination accuracy of heart rate variability, more calculation resources are not consumed, initial heart rate data corresponding to a target user can be acquired firstly, initial curve peak information is determined based on the initial heart rate data, then a data window is created for the initial curve peak information, interpolation processing is conducted on the data window, so that the target heart rate data is obtained, after the client collects the initial heart rate data with lower frequency, the initial heart rate data can be improved to be high frequency capable of calculating heart rate variability in an interpolation mode, calculation of heart rate variability is conducted based on the high frequency, calculation accuracy can be effectively improved, more types of clients can be adapted, and universality is higher. After that, the peak information of the target curve corresponding to the target heart rate data can be determined, then the peak interval sequence is constructed according to the peak information of the target curve, and the peak interval sequence is updated as the target peak interval sequence based on the peak information, so that the peak interval parameters which are not matched with the real heart rate variation of the user are removed, finally, the heart rate variability of the target user is calculated by utilizing the peak interval parameters contained in the target peak interval sequence, the calculation accuracy of the heart rate variability is effectively improved, meanwhile, the consumption of calculation resources can be effectively reduced, the accurate calculation of the heart rate variability can be completed on any type of equipment, the monitoring requirement of the health information of the user is met, and more stable and accurate health management service is provided for the user.

The above is a schematic solution of a data processing apparatus of the present embodiment. It should be noted that, the technical solution of the data processing apparatus and the technical solution of the data processing method belong to the same conception, and details of the technical solution of the data processing apparatus, which are not described in detail, can be referred to the description of the technical solution of the data processing method.

Fig. 5 illustrates a block diagram of a computing device 500 provided in accordance with an embodiment of the present specification. The components of the computing device 500 include, but are not limited to, a memory 510 and a processor 520. Processor 520 is coupled to memory 510 via bus 530 and database 550 is used to hold data.

Computing device 500 also includes access device 540, access device 540 enabling computing device 500 to communicate via one or more networks 560. Examples of such networks include public switched telephone networks (PSTN, public Switched Telephone Network), local area networks (LAN, local Area Network), wide area networks (WAN, wide Area Network), personal area networks (PAN, personal Area Network), or combinations of communication networks such as the internet. The access device 540 may include one or more of any type of network interface, wired or wireless (e.g., network interface card (NIC, network interface controller)), such as an IEEE802.11 wireless local area network (WLAN, wireless Local Area Network) wireless interface, a worldwide interoperability for microwave access (Wi-MAX, worldwide Interoperability for Microwave Access) interface, an ethernet interface, a universal serial bus (USB, universal Serial Bus) interface, a cellular network interface, a bluetooth interface, a near field communication (NFC, near Field Communication) interface, and so forth.

In one embodiment of the present application, the above-described components of computing device 500, as well as other components not shown in FIG. 5, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device illustrated in FIG. 5 is for exemplary purposes only and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as desired.

Computing device 500 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smart phone), wearable computing device (e.g., smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or personal computer (PC, personal Computer). Computing device 500 may also be a mobile or stationary server.

Wherein the processor 520 is configured to implement the steps of the data processing method described above when executing computer-executable instructions.

The foregoing is a schematic illustration of a computing device of this embodiment. It should be noted that, the technical solution of the computing device and the technical solution of the data processing method belong to the same concept, and details of the technical solution of the computing device, which are not described in detail, can be referred to the description of the technical solution of the data processing method.

An embodiment of the present disclosure also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, are used in a data processing method.

The above is an exemplary version of a computer-readable storage medium of the present embodiment. It should be noted that, the technical solution of the storage medium and the technical solution of the data processing method belong to the same concept, and details of the technical solution of the storage medium which are not described in detail can be referred to the description of the technical solution of the data processing method.

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.

The computer instructions include computer program code that may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

It should be noted that, for the sake of simplicity of description, the foregoing method embodiments are all expressed as a series of combinations of actions, but it should be understood by those skilled in the art that the present description is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present description. Further, those skilled in the art will appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily all necessary in the specification.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.

The preferred embodiments of the present specification disclosed above are merely used to help clarify the present specification. Alternative embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the disclosure and the practical application, to thereby enable others skilled in the art to best understand and utilize the disclosure. This specification is to be limited only by the claims and the full scope and equivalents thereof.

Claims

1. A data processing method, applied to a client, comprising:

2. The method of claim 1, wherein the determining initial curve peak information corresponding to the initial heart rate data comprises:

3. The method according to claim 2, wherein the preprocessing the initial heart rate data, and determining curve information corresponding to the initial heart rate data according to the preprocessing result, includes:

4. A method according to claim 2 or 3, wherein said generating initial curve peak information corresponding to said initial heart rate data based on said curve information comprises:

5. The method according to claim 1, wherein the creating a data window for the initial curve peak information, interpolating the data window, and generating target heart rate data according to the processing result includes:

6. The method of claim 1, wherein said constructing a peak-to-peak spacing sequence from said target curve peak information comprises:

7. The method of claim 6, wherein said constructing said sequence of peak-to-peak intervals based on said absolute position information comprises:

8. The method of claim 1, wherein said updating said sequence of peak-to-peak intervals to a sequence of target peak-to-peak intervals comprises:

9. The method of claim 8, wherein updating the sequence of peak-to-peak intervals based on the abnormal peak-to-peak interval parameter to obtain the sequence of target peak-to-peak intervals comprises:

10. The method of claim 8, wherein updating the sequence of peak-to-peak intervals based on the abnormal peak-to-peak interval parameter to obtain the sequence of target peak-to-peak intervals comprises:

11. A data processing apparatus, for application to a client, comprising:

12. A computing device comprising a memory and a processor; the memory is configured to store computer executable instructions and the processor is configured to execute the computer executable instructions to implement the steps of the method of any one of claims 1 to 10.

13. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 10.