CN114366060A - Health early warning method and device based on heart rate variability and electronic equipment - Google Patents
Health early warning method and device based on heart rate variability and electronic equipment Download PDFInfo
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- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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Abstract
The application provides a health early warning method, a health early warning device and electronic equipment based on heart rate variability, wherein the method comprises the following steps: monitoring heart rate time sequence data of a user based on physical sign data acquisition equipment worn by the user; extracting heart rate variability features from the heart rate time series data; and inputting the heart rate variability characteristics into a preset health early warning model to obtain a health early warning result of the user. According to the method provided by the scheme, the heart rate variability characteristics of the user are monitored in real time, the current health early warning result of the user is determined based on the health early warning model, and the real-time monitoring of the human health is realized, so that the user can be helped to know the self health state in time.
Description
Technical Field
The application relates to the technical field of data monitoring, in particular to a health early warning method and device based on heart rate variability and electronic equipment.
Background
The heart rate variability is slight change between two heartbeats, the heart rate time sequence data is nonstationary time sequence data, and the heart rate variability can better reflect the current human body change conditions.
In the prior art, it is common for a doctor to analyze the heart rate variability of a patient according to the heart rate detection report of the patient.
However, the current heart rate variability analysis process is complicated and needs to be realized by medical equipment, so that the real-time monitoring of human health cannot be realized.
Disclosure of Invention
The application provides a health early warning method and device based on heart rate variability and electronic equipment, and aims to overcome the defects that real-time monitoring of human health cannot be achieved in the prior art and the like.
In a first aspect, the present application provides a health pre-warning method based on heart rate variability, including:
monitoring heart rate time sequence data of a user based on physical sign data acquisition equipment worn by the user;
extracting heart rate variability features from the heart rate time series data;
and inputting the heart rate variability features into a preset health early warning model to obtain a health early warning result of the user.
Optionally, the heart rate variability features include time domain features, and extracting the heart rate variability features from the heart rate time series data includes:
performing time domain analysis on the heart rate time sequence data to determine time domain characteristics of the heart rate time sequence data; wherein the time domain features characterize a heart rate variability feature embodied by a sequence of two heartbeat time intervals in the heart rate timing data.
Optionally, the heart rate variability features include frequency domain features, and extracting heart rate variability features from the heart rate time series data includes:
and performing frequency domain analysis on the heart rate time sequence data to determine the frequency domain characteristics of the heart rate time sequence data.
Optionally, the performing frequency domain analysis on the heart rate time series data to determine the frequency domain characteristics of the heart rate time series data includes:
detecting very low frequencies, low frequencies and high frequencies in a heart rate variability power spectrum characterized by the heart rate timing data;
and determining the frequency domain characteristics of the heart rate time sequence data according to the power ratio and the normalization value among the extremely low frequency, the low frequency and the high frequency.
Optionally, the heart rate variability feature comprises a non-linear feature, and extracting the heart rate variability feature from the heart rate time series data comprises:
and carrying out nonlinear analysis on the heart rate time sequence data to determine the nonlinear characteristics of the heart rate time sequence data.
Optionally, the non-linear feature includes a sample entropy and/or detrending fluctuation analysis result, and the performing non-linear analysis on the heart rate time series data to determine the non-linear feature of the heart rate time series data includes:
calculating sample entropy of the heart rate time series data;
and/or performing detrending fluctuation analysis on the heart rate time series data to obtain a detrending fluctuation analysis result of the heart rate time series data.
Optionally, before extracting heart rate variability features from the heart rate time series data, the method further comprises:
and preprocessing the heart rate time series data to eliminate noise and abnormal values of the heart rate time series data.
A second aspect of the present application provides a health-warning device based on heart rate variability, comprising:
the monitoring module is used for monitoring the heart rate time sequence data of the user based on the physical sign data acquisition equipment worn by the user;
the characteristic extraction module is used for extracting heart rate variability characteristics from the heart rate time sequence data;
and the early warning module is used for inputting the heart rate variability characteristics into a preset health early warning model so as to obtain a health early warning result of the user.
Optionally, the heart rate variability features include time domain features, and the feature extraction module is specifically configured to:
performing time domain analysis on the heart rate time sequence data to determine time domain characteristics of the heart rate time sequence data; wherein the time domain features characterize a heart rate variability feature embodied by a sequence of two heartbeat time intervals in the heart rate timing data.
Optionally, the heart rate variability features include frequency domain features, and the feature extraction module is specifically configured to:
and performing frequency domain analysis on the heart rate time sequence data to determine the frequency domain characteristics of the heart rate time sequence data.
Optionally, the feature extraction module is specifically configured to:
detecting very low frequencies, low frequencies and high frequencies in a heart rate variability power spectrum characterized by the heart rate timing data;
and determining the frequency domain characteristics of the heart rate time sequence data according to the power ratio and the normalization value among the extremely low frequency, the low frequency and the high frequency.
Optionally, the heart rate variability features include non-linear features, and the feature extraction module is specifically configured to:
and carrying out nonlinear analysis on the heart rate time sequence data to determine the nonlinear characteristics of the heart rate time sequence data.
Optionally, the nonlinear features include sample entropy and/or detrending fluctuation analysis results, and the feature extraction module is specifically configured to:
calculating sample entropy of the heart rate time series data;
and/or performing detrending fluctuation analysis on the heart rate time series data to obtain a detrending fluctuation analysis result of the heart rate time series data.
Optionally, the apparatus further comprises:
and the data preprocessing module is used for preprocessing the heart rate time sequence data so as to eliminate noise and abnormal values of the heart rate time sequence data.
A third aspect of the present application provides an electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the method as set forth in the first aspect above and in various possible designs of the first aspect.
A fourth aspect of the present application provides a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement a method as set forth in the first aspect and various possible designs of the first aspect.
This application technical scheme has following advantage:
the application provides a health early warning method, a health early warning device and electronic equipment based on heart rate variability, wherein the method comprises the following steps: monitoring heart rate time sequence data of a user based on physical sign data acquisition equipment worn by the user; extracting heart rate variability features from the heart rate time series data; and inputting the heart rate variability characteristics into a preset health early warning model to obtain a health early warning result of the user. According to the method provided by the scheme, the heart rate variability characteristics of the user are monitored in real time, the current health early warning result of the user is determined based on the health early warning model, and the real-time monitoring of the human health is realized, so that the user can be helped to know the self health state in time.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art according to these drawings.
Fig. 1 is a schematic structural diagram of a health early warning system according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a health warning method based on heart rate variability according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a health warning device based on heart rate variability according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. In the description of the following examples, "plurality" means two or more unless specifically limited otherwise.
Some researches show that before and after diseases such as cardiovascular and respiratory infectious diseases and the like occur, physical sign data can change obviously, wherein heart rate data can have certain difference, and based on the difference, some methods exist for early warning whether some people have disease risks or not by analyzing the physical sign data such as heart rate, body temperature, breathing and the like. The heart rate variability is a slight change between two heartbeats, the heart rate time sequence data is non-stationary time sequence data, and the slight change cannot be well captured by simply using some time domain and frequency domain characteristics, especially on the heart rate data with short time sequence.
In order to solve the above problems, an embodiment of the present application provides a health early warning method, device and electronic device based on heart rate variability, where the method includes: monitoring heart rate time sequence data of a user based on physical sign data acquisition equipment worn by the user; extracting heart rate variability features from the heart rate time series data; and inputting the heart rate variability characteristics into a preset health early warning model to obtain a health early warning result of the user. According to the method provided by the scheme, the heart rate variability characteristics of the user are monitored in real time, the current health early warning result of the user is determined based on the health early warning model, and the real-time monitoring of the human health is realized, so that the user can be helped to know the self health state in time.
The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
First, a structure of a health warning system based on the present application is explained:
the health early warning method and device based on heart rate variability and the electronic equipment are suitable for detecting the body health state of a user. As shown in fig. 1, the health early warning system according to the embodiment of the present application is a schematic structural diagram, and mainly includes a physical sign data acquisition device worn by a user and a health early warning device based on heart rate variability. Specifically, the sign data acquisition equipment is used for acquiring heart rate time sequence data of a user and sending the acquired heart rate time sequence data to the health early warning device based on heart rate variability, and the device determines a health early warning result of the user according to the acquired heart rate time sequence data.
The embodiment of the application provides a health early warning method based on heart rate variability, which is used for detecting the physical health state of a user so as to realize health early warning. The execution subject of the embodiment of the present application is an electronic device, such as a server, a desktop computer, a notebook computer, a tablet computer, and other electronic devices that can be used for analyzing heart rate timing data.
As shown in fig. 2, a schematic flow chart of a health warning method based on heart rate variability provided in an embodiment of the present application is shown, where the method includes:
Wherein, sign data acquisition device can be the intelligent bracelet that possesses rhythm of the heart chronogenesis data monitor function.
Wherein the heart rate variability features comprise at least time domain features, frequency domain features and non-linear features of the heart rate time series data.
Specifically, for different types of heart rate variability features, a corresponding feature extraction manner may be adopted to extract the relevant features.
It should be noted that the health early warning model may also be referred to as a disease risk level prediction model, which may implement early warning of different types of diseases, and may output disease risk levels (health early warning results) of the user in different diseases by using the heart rate variability characteristics of the user as the input characteristics of the model.
The health early warning model can be deployed at the cloud end to provide health early warning service for a plurality of users at the same time.
Specifically, the extracted heart rate variability features can be input into a health early warning model F (-) as model input features for prediction, and the following mathematical function expression relationship is shown:
y＝F(X)
wherein, X represents the heart rate variability feature, and the numerical value is represented by an n × m matrix, where n represents the number of people to be predicted, m is the dimension of the heart rate variability feature, for example, m is 16, and y is the model output result (health early warning result).
Specifically, F (-) can be a binary model or a regression model. If the model is a binary classification model, the output y is a value of 0 or 1, and whether the expression is a disease patient, such as a respiratory disease infected patient, and the like; if the regression model is selected, the output y value is a continuous value, which expresses the risk of the user.
Based on the foregoing embodiments, as an implementable manner, in an embodiment, when the heart rate variability feature includes a time-domain feature, time-domain analysis may be performed on the heart rate time-series data to determine the time-domain feature of the heart rate time-series data.
Wherein the time domain features characterize a heart rate variability feature embodied by a sequence of two heartbeat time intervals in the heart rate timing data.
Specifically, the Time domain analysis (HRV) method of Heart Rate Variability (HRV) refers to calculating a statistical indicator reflecting sequence variability from an RR interval sequence, which refers to the Time interval of two heartbeats. These measures are easy to implement and are computationally inexpensive. Typically from RR time intervals in a selected time window (between 0.5 and 5 minutes more). The embodiment of the application provides 10 time domain features applicable to health early warning, which are detailed in table 1.
TABLE 1 HRV temporal features
Wherein SDNN refers to the standard deviation of RR interval sequences, reflecting the global variability of the sequences. SDANN is the standard deviation of the mean of these fragments, while SDNNIDX is the mean of the standard deviation of these fragments, SDANN assesses long-term variability, SDNNIDX quantifies short-term variability.
NN50, PNN50, SDSD, and RMSSD are all to quantify differences between adjacent RR interval values. NN50 is the number of RR interval sequences that differ from their previous value by more than 50 milliseconds, while PNN50 is the percentage of differences by more than 50 milliseconds. SDSD is the standard deviation of the difference between adjacent RR intervals, and RMSSD is the square root of the mean of the sum of the squares of the differences of adjacent RR intervals.
IRRR is the difference between the upper and lower quartiles of the quantization, i.e. the difference between the first and third quartile of the RR interval sequence. MADRR is the median of the absolute difference of adjacent RR intervals, but there is no saturation problem like PNN 50.
Further, in an embodiment, when the heart rate variability features comprise frequency domain features, the heart rate time series data may be subjected to a frequency domain analysis to determine the frequency domain features of the heart rate time series data.
It should be noted that the frequency domain analysis of the HRV mainly performs Power spectral Density estimation (PSD). Three components with physiological relativity in the HRV power spectrum are Very Low Frequency (Very Low Frequency, abbreviated as VLF, 0.003-0.03 Hz), Low Frequency (Low Frequency, abbreviated as LF, 0.03-0.15 Hz) and High Frequency (High Frequency, abbreviated as HF, 0.15-0.4 Hz).
In particular, in an embodiment, very low frequencies, low frequencies and high frequencies in a heart rate variability power spectrum characterized by said heart rate time series data may be detected; and determining the frequency domain characteristics of the heart rate time sequence data according to the power ratio and the normalization value among the extremely low frequency, the low frequency and the high frequency.
Wherein, LF/HF is the ratio of the spectral power of the low and high frequency bands, which can be regarded as a quantitative index for measuring the balance of the sympathetic nerve and the vagus nerve; a higher LF/HF value indicates a predominance of the sympathetic nervous system, while a lower LF/HF value indicates a predominance of the parasympathetic nervous system. LFn is the ratio of the spectral power of the low frequency band to the spectral power of the low and high frequency bands, and LFn is LF/(LF + HF). HFn is the ratio of the spectrum power of the high frequency band to the spectrum power of the low and high frequency bands, HFn is HF/(LF + HF), VLFn is the ratio of the spectrum power of the very low frequency band to the total spectrum power after the ultra low frequency band is removed, VLFn is VLF/(VLF + LF + HF).
Specifically, the power ratio or the normalized value of each frequency band can be calculated to replace direct calculation of the power value of each frequency band, so that the frequency domain feature is more robust.
Further, in an embodiment, when the heart rate variability characteristic comprises a non-linear characteristic, the heart rate time series data may be subjected to a non-linear analysis to determine the non-linear characteristic of the heart rate time series data.
It should be noted that, because the specific extraction condition of the nonlinear features is affected by the nonlinear analysis method, that is, the characteristics of the nonlinear features obtained by different nonlinear analysis methods are different, in order to improve the accuracy of the health early warning result, the nonlinear features provided in the embodiment of the present application at least include the sample entropy and/or the detrending fluctuation analysis result.
Accordingly, a sample entropy of the heart rate timing data may be calculated; and/or performing detrending fluctuation analysis on the heart rate time series data to obtain a detrending fluctuation analysis result of the heart rate time series data.
It should be noted that the sample entropy is a measurement method of time series complexity different from the approximate entropy, is similar to the approximate entropy, but does not calculate the self-matching degree, can effectively overcome the deviation of approximate entropy calculation, has better consistency with the approximate entropy, has small dependence of the sample entropy calculation on the length of the data sequence, can achieve the purpose of effective analysis only by a shorter data sequence, and has faster calculation speed and higher precision. Sample entropy is also used to quantify the complexity and regularity of time series data (e.g., heart rate time series data), and the larger the sample entropy, the lower the self-similarity of the time series data, and the more complex the sequence. The sample entropy is calculated as follows:
(a) the method comprises the following steps A group of m-dimensional vector sequences is formed by a time sequence data (heart rate time sequence data) X (t) with N points according to sequence numbers: xm(i)＝{xm(i)，xm(i+1)，…，xm(i + m-1) }, i ═ 1, 2, …, N-m +1, vector Xm(i) Represents a sequence segment of m continuous points from the ith point in the data sequence X (t).
(b) The method comprises the following steps Definition of Xm(i) And Xm(j) Distance d [ X ] betweenm(i)，Xm(j)]The element with the largest difference between the two corresponding elements, namely:
for each value of i, X is calculatedm(i) And the remaining vector Xm(j) Distance d [ X ] betweenm(i)，Xm(j)]Where i is 1, 2, …, N-m +1, j is 1, 2, …, N-m +1 and j ≠ i.
(c) The method comprises the following steps For a given threshold r, d [ X ] is satisfied for each value of im(i)，Xm(j)]Number of < r, noted
Then calculating the ratio of the total number N-m of the distance between the calculated value and the corresponding value i, and recording the ratio as the value
Namely:
(d) the method comprises the following steps Under all the i values
I.e.:
(e) the method comprises the following steps Increasing the original m dimension to m +1 dimension, repeating the steps (a-d) to obtain:
(f) the method comprises the following steps The sample entropy of this time series is defined as:
when N takes a finite value, the estimated value of the sample entropy of the heart rate time series data is as follows:
the value of SampEn is related to the values of m and r, but the sample entropy has good consistency, and the trend of the decrease and increase of the sample entropy value of the heart rate time sequence data is not influenced by the change of the values of m and r. In general, m may be 1, 2, and r may be 0.1 to 0.25SD, where SD is the standard deviation of the heart rate time series data x (t).
It should be further noted that Detrended Fluctuation Analysis (DFA) is a method based on the random walk theory, and is an improvement of the root mean square Analysis method applied to the random walk of non-stationary signals. The time series were summed at different observation windows, trended off, and plotted on a log-log scale against the size of the observation window. The purpose of these operations is to obtain fluctuation information inherent to sequence data (heart rate time series data) and to eliminate noise caused by an external environment appearing as a trend in the data. The DFA can be used for quantifying the fractal scale characteristics of the RR interval sequences with short lengths, detecting the long-range power function correlation of the RR interval sequences and obtaining the internal fluctuation of the RR interval sequences with polynomial noise removed.
For example, assuming an RR interval sequence of length N, the DFA is calculated as follows:
(a) the method comprises the following steps Constructing a new sequence y (k) that is summed after mean removal:
where y (k) is the kth value of the new sequence, RR (i) is the ith value of the RR interval sequence,
is the average heart beat time interval of the whole RR interval sequence.
(b) The method comprises the following steps Dividing new sequences into
Non-overlapping sequence segments of equal length and fitting the local trend y of each segmentn(j, s), trend yn(j, s) is a fitting polynomial of the s-th sequence segment, and in the embodiment of the application, the fitting is performed by using a least square method, and the y coordinate of the linear segment obtained by fitting is yn(j, s).
(c) The method comprises the following steps For each sequence fragment, its trend was removed and the variance was calculated:
(d) the method comprises the following steps Calculate all NnRoot mean square of individual sequence segments to obtain DFA fluctuation function
Repeating the above process (a-d) at different values of n to obtain a relationship between F (n) and n, wherein F (n) increases with increasing n, substantially satisfying F (n) to nαAnd alpha is expressed as a DFA index, and different alpha values represent different correlations inherent to the original RR interval sequence. Further, f (n) and n are plotted on a log-log scale, and the slope of the curve is calculated by linear fitting, i.e., the value of α (detrended fluctuation analysis).
Specifically, the heart rate variability features obtained based on the feature extraction method provided by the above embodiment include 10 time domain features, 4 frequency domain features and 2 nonlinear features, that is, the heart rate variability features used in the embodiment of the present application are 16-dimensional, and the sample entropy and detrending fluctuation analysis results represent HRV analysis results of a shorter time series, so that the change of the heart rate in a shorter time range can be better captured, and the timeliness of the health early warning results is improved.
On the basis of the above embodiments, since the quality of the directly acquired heart rate time series data is difficult to guarantee, as an implementable manner, in an embodiment, before extracting heart rate variability features from the heart rate time series data, the heart rate time series data may be preprocessed to eliminate noise and abnormal values of the heart rate time series data.
Specifically, a preset data filter can be adopted to preprocess the heart rate time series data so as to eliminate noise and abnormal values of the heart rate time series data, and a foundation is laid for improving the reliability of the heart rate variability feature extraction result.
According to the health early warning method based on heart rate variability, the heart rate time sequence data of a user is monitored through physical sign data acquisition equipment worn by the user; extracting heart rate variability features from the heart rate time series data; and inputting the heart rate variability characteristics into a preset health early warning model to obtain a health early warning result of the user. According to the method provided by the scheme, the heart rate variability characteristics of the user are monitored in real time, the current health early warning result of the user is determined based on the health early warning model, and the real-time monitoring of the human health is realized, so that the user can be helped to know the self health state in time. And moreover, a method for taking sample entropy calculation and trend-free fluctuation analysis as heart rate variability analysis is provided and applied to health early warning. The sample entropy calculation and the trend-removing fluctuation analysis are two nonlinear methods suitable for HRV analysis of a short time sequence, so that the change of the heart rate in a short time range can be captured better, and the timeliness of the health early warning result is improved.
The embodiment of the application provides a health early warning device based on heart rate variability, which is used for executing the health early warning method based on heart rate variability provided by the embodiment.
Fig. 3 is a schematic structural diagram of a health warning device based on heart rate variability according to an embodiment of the present disclosure. The health-warning device 30 based on heart rate variability includes: a monitoring module 301, a feature extraction module 302 and an early warning module 303.
The monitoring module is used for monitoring the heart rate time sequence data of the user based on physical sign data acquisition equipment worn by the user; the characteristic extraction module is used for extracting heart rate variability characteristics from the heart rate time sequence data; and the early warning module is used for inputting the heart rate variability characteristics into a preset health early warning model so as to obtain a health early warning result of the user.
Specifically, in an embodiment, the heart rate variability features include time domain features, and the feature extraction module is specifically configured to:
performing time domain analysis on the heart rate time sequence data to determine time domain characteristics of the heart rate time sequence data; wherein the time domain features characterize a heart rate variability feature embodied by a sequence of two heartbeat time intervals in the heart rate timing data.
In particular, in an embodiment, the heart rate variability features comprise frequency domain features, and the feature extraction module is particularly configured to:
and performing frequency domain analysis on the heart rate time sequence data to determine the frequency domain characteristics of the heart rate time sequence data.
Specifically, in an embodiment, the feature extraction module is specifically configured to:
detecting very low frequencies, low frequencies and high frequencies in a heart rate variability power spectrum characterized by the heart rate timing data;
and determining the frequency domain characteristics of the heart rate time sequence data according to the power ratio and the normalization value among the extremely low frequency, the low frequency and the high frequency.
In particular, in an embodiment, the heart rate variability features comprise non-linear features, the feature extraction module being in particular configured to:
and carrying out nonlinear analysis on the heart rate time sequence data to determine the nonlinear characteristics of the heart rate time sequence data.
Specifically, in an embodiment, the nonlinear features include sample entropy and/or detrended fluctuation analysis results, and the feature extraction module is specifically configured to:
calculating sample entropy of the heart rate time series data;
and/or performing detrending fluctuation analysis on the heart rate time series data to obtain a detrending fluctuation analysis result of the heart rate time series data.
Specifically, in one embodiment, the apparatus further comprises:
and the data preprocessing module is used for preprocessing the heart rate time sequence data so as to eliminate noise and abnormal values of the heart rate time sequence data.
With regard to the heart rate variability-based health-warning device in the present embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be described in detail here.
The health early warning device based on heart rate variability provided by the embodiment of the application is used for executing the health early warning method based on heart rate variability provided by the embodiment of the application, the implementation mode and the principle are the same, and the repeated description is omitted.
The embodiment of the application provides an electronic device, which is used for executing the health early warning method based on heart rate variability provided by the embodiment.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 40 includes: at least one processor 41 and a memory 42.
The memory stores computer-executable instructions; the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the heart rate variability-based wellness warning method provided by the above embodiments.
The electronic device provided by the embodiment of the application is used for executing the health early warning method based on heart rate variability provided by the embodiment, and the implementation manner and the principle of the method are the same and are not repeated.
The embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the method for health early warning based on heart rate variability provided in any of the above embodiments is implemented.
The storage medium containing the computer-executable instructions of the embodiment of the present application may be used to store the computer-executable instructions of the health early warning method based on heart rate variability provided in the foregoing embodiment, and the implementation manner and the principle thereof are the same and are not described again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A health early warning method based on heart rate variability is characterized by comprising the following steps:
monitoring heart rate time sequence data of a user based on physical sign data acquisition equipment worn by the user;
extracting heart rate variability features from the heart rate time series data;
and inputting the heart rate variability features into a preset health early warning model to obtain a health early warning result of the user.
2. The method of claim 1, wherein the heart rate variability features comprise time domain features, and wherein extracting heart rate variability features from the heart rate time series data comprises:
performing time domain analysis on the heart rate time sequence data to determine time domain characteristics of the heart rate time sequence data; wherein the time domain features characterize a heart rate variability feature embodied by a sequence of two heartbeat time intervals in the heart rate timing data.
3. The method of claim 1, wherein the heart rate variability features comprise frequency domain features, and wherein extracting heart rate variability features from the heart rate time series data comprises:
and performing frequency domain analysis on the heart rate time sequence data to determine the frequency domain characteristics of the heart rate time sequence data.
4. The method of claim 3, wherein the performing a frequency domain analysis on the heart rate time series data to determine frequency domain characteristics of the heart rate time series data comprises:
detecting very low frequencies, low frequencies and high frequencies in a heart rate variability power spectrum characterized by the heart rate timing data;
and determining the frequency domain characteristics of the heart rate time sequence data according to the power ratio and the normalization value among the extremely low frequency, the low frequency and the high frequency.
5. The method of claim 1, wherein the heart rate variability features comprise non-linear features, and wherein extracting heart rate variability features from the heart rate time series data comprises:
and carrying out nonlinear analysis on the heart rate time sequence data to determine the nonlinear characteristics of the heart rate time sequence data.
6. The method of claim 5, wherein the non-linear characteristics include sample entropy and/or detrended fluctuation analysis results, and wherein the non-linear analysis of the heart rate time series data to determine the non-linear characteristics of the heart rate time series data comprises:
calculating sample entropy of the heart rate time series data;
and/or performing detrending fluctuation analysis on the heart rate time series data to obtain a detrending fluctuation analysis result of the heart rate time series data.
7. The method of claim 1, wherein prior to extracting heart rate variability features from the heart rate time series data, the method further comprises:
and preprocessing the heart rate time series data to eliminate noise and abnormal values of the heart rate time series data.
8. A health-warning device based on heart rate variability, comprising:
the monitoring module is used for monitoring the heart rate time sequence data of the user based on the physical sign data acquisition equipment worn by the user;
the characteristic extraction module is used for extracting heart rate variability characteristics from the heart rate time sequence data;
and the early warning module is used for inputting the heart rate variability characteristics into a preset health early warning model so as to obtain a health early warning result of the user.
9. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of any of claims 1-7.
10. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a processor, implement the method of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115067930A (en) * | 2022-08-22 | 2022-09-20 | 华南师范大学 | Early warning method and device for respiratory state, computer equipment and storage medium |
| CN116269291A (en) * | 2023-05-26 | 2023-06-23 | 深圳市魔样科技有限公司 | Intelligent watch monitoring system and method based on artificial intelligence |
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| CN109350020A (en) * | 2018-11-21 | 2019-02-19 | 新绎健康科技有限公司 | Psychosomatic health analytical equipment and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109350020A (en) * | 2018-11-21 | 2019-02-19 | 新绎健康科技有限公司 | Psychosomatic health analytical equipment and method |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115067930A (en) * | 2022-08-22 | 2022-09-20 | 华南师范大学 | Early warning method and device for respiratory state, computer equipment and storage medium |
| CN115067930B (en) * | 2022-08-22 | 2022-11-08 | 华南师范大学 | Breathing state early warning method and device, computer equipment and storage medium |
| CN116269291A (en) * | 2023-05-26 | 2023-06-23 | 深圳市魔样科技有限公司 | Intelligent watch monitoring system and method based on artificial intelligence |
| CN116269291B (en) * | 2023-05-26 | 2023-07-25 | 深圳市魔样科技有限公司 | Intelligent watch monitoring system and method based on artificial intelligence |
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