WO2021042591A1

WO2021042591A1 - Heart rate variability signal analysis method based on extremum energy decomposition method

Info

Publication number: WO2021042591A1
Application number: PCT/CN2019/121416
Authority: WO
Inventors: 周作建; 宁新宝; 曾彭; 姜晓东; 王�华; 王斌斌; 刘红星
Original assignee: 江苏华康信息技术有限公司
Priority date: 2019-09-06
Filing date: 2019-11-28
Publication date: 2021-03-11
Also published as: AU2019101756A4; CN110464337B; CN110464337A

Abstract

A heart rate variability signal analysis method based on an extremum energy decomposition method, comprising obtaining an ECG signal in an unknown state at a given time and a given sampling frequency, and denoising the ECG signal to obtain an RRI signal x(t); using the RRI signal x(t) as an original signal, and decomposing the original signal x(t) into n extremum mode function components and one margin, the n extremum mode function components obtained by decomposing the original signal x(t) representing components of the original signal in different frequency bands; and determining, according to the n extremum mode function components, whether the RRI signal is an abnormal heart rate variability signal. According to the present invention, the RRI signal is analyzed using an extremum energy decomposition method, the original signal is decomposed into a plurality of components, i.e., an extremum component function, and energy of each component is calculated to obtain energy distribution thereof.

Description

A Heart Rate Variability Signal Analysis Method Based on Extreme Energy Decomposition Method

Technical field

The invention relates to an electrocardiogram signal analysis, in particular to a heart rate variability signal analysis method based on an extreme value energy decomposition method.

Background technique

Physiological signals are generated by the interaction of multiple systems in a living body, and the time and intensity of the action of different systems are different, leading to the complexity of time and space in physiological signals. Heart rate variability (HRV) refers to the measurement of the time variability between consecutive cardiac cycles, to be precise, it should be the measurement of the variance of the difference between the normal P-P intervals that occur continuously. However, because the P wave is not as obvious as the R wave or the top of the P wave is sometimes wide and blunt, the research on the heart rate variability signal is usually replaced by the R-R interval signal (RRI) equal to the P-P interval. Studies have shown that HRV can be used as a non-invasive detection indicator of autonomic nervous system activity, especially in judging the prognosis of certain cardiovascular diseases.

In the prior art, the heart rhythm variability signal (HRV signal) is often used as an analysis object to study the long-term regulation rhythm of the heart (<1 Hz). A large number of studies have shown that human HRV signals have long-term correlation and nonlinear dynamics complexity, and age and disease will lead to reduced dynamics complexity. Research on Heart Rate Variability (HRV) signals is commonly used in the RR interval (Interbeat Intervals, RRI) signal, that is, the time interval signal between the R waves of consecutive RRI signals.

The most commonly used method to study the energy changes of HRV signals is power spectrum analysis (PSD). PSD converts the power of the HRV signal into a function of frequency through Fourier transform, and studies the power of different frequency domains. Generally, the HRV spectrum is divided into high frequency (HF), low frequency (LF) and very low frequency (VLF). Frequency band. The LF/HF ratio has important clinical value. Heart disease can cause changes in the power spectrum of HRV, such as heart failure and myocardial infarction that cause normalized HF to increase, and LF and VLF to decrease. However, PSD is not a data-driven method, and the frequency domain segmentation is relatively rough, resulting in missing details and insufficient segmentation.

Therefore, it is urgent to solve the above-mentioned problems.

Summary of the invention

Object of the invention: The object of the present invention is to provide a heart rate variability signal analysis method based on extreme energy decomposition method that can use less data and can intuitively reflect the true law of the energy distribution and signal fluctuation of the electrocardiogram.

Technical solution: In order to achieve the above objective, the present invention discloses a heart rate variability signal analysis method based on extreme energy decomposition method, which includes the following steps:

(1) Obtain the ECG signal of the unknown state at a given time and a given sampling frequency, and then perform denoising preprocessing on the ECG signal, extract the RRI signal from it, and obtain the RRI signal x(t) of the unknown state;

(2) Take the RRI signal x(t) as the original signal, find all the local extreme points of the original signal, and then connect all the maximum points and all the minimum points of the original signal using spline curves to form them respectively The upper envelope e _max and the lower envelope e _{min are used} to obtain the envelope mean signal m(t)=(e _max +e _min )/2 of the upper and lower envelopes;

(3) Subtract the envelope mean signal m(t) from the original signal x(t) to obtain h(t)=x(t)-m(t); then judge whether h(t) satisfies the extreme mode If the judgment condition of the function is not met, return h(t) as the original signal to step (3) until h _k (t) meets the judgment condition of the extreme modal function, then write c ₁ (t) = h _k (t), as the first extreme value modal function component;

(4) Subtract the first extremum modal function component c ₁ (t) from the _{original signal x(t) to obtain the margin r 1} (t) = x(t)-c ₁ (t), and then judge Whether h _k (t) meets the stopping criterion, if not, take r ₁ (t) as the new original sequence x(t), and return to steps (2) and (3); if h _k (t) meets the stopping criterion But when n<8, return to step (1) to obtain the original signal again; if h _k (t) meets the stopping criterion and n≥8, the second, third, ..., nth extreme modal function components and margins are obtained r _n (t), then the original signal x(t) is decomposed into n extreme value modal function components and a margin, namely

(5) Perform frequency spectrum analysis on the extreme value modal function components c _i (t), i=1, 2,..., n to obtain the center frequency of each extreme value modal function component;

(6). The n extreme value modal function components decomposed from the original signal x(t) represent the components of the original signal in different frequency bands, and then the energy of each component is calculated

E _i ＝∫|c _i (t)| ² dt,i＝1,2,...,n

Normalize each energy value to get a normalized energy distribution vector

p _i ＝E _i /E,i＝1,2,...,n

among them,

The first component p ₁ represents the energy of the highest frequency band, which represents the proportion of the energy distribution of the signal in the highest frequency range, and the last component p _n represents the proportion of the energy distribution of the signal in the lowest frequency range; according to the normalized energy distribution vector Draw a normalized energy distribution map, where the abscissa represents the component level, the ordinate represents the normalized energy distribution vector value, the curve represents the average value, and the error bar represents the standard deviation;

(7). Select the second component p ₂ to the seventh component p ₇ and calculate the energy difference value EDV, EDV=(p ₂ +p ₃ +p ₄ )-(p ₅ +p ₆ +p ₇ ), when When EDV ≤ the first standard value, it is determined that the RRI signal is a normal heart rate variability signal. When the first standard value <EDV <the second standard value, it is determined that the RRI signal is a suspected abnormal heart rate variability signal, when EDV ≥ At the second standard value, it is determined that the RRI signal is an abnormal heart rate variability signal.

Wherein, the minimum amount of data required for the original signal x(t) is N=2 ⁿ⁺¹ , where n is the number of decomposed extreme value modal function components.

Preferably, the specific method of denoising preprocessing in the step (1) is: filtering the ECG signal through a 40Hz zero-phase FIR low-pass filter to eliminate high-frequency noise, and then through a median filter to remove baseline drift.

Furthermore, the conditions for determining the extreme value modal function in the step (3) are: (a). In the entire data sequence, the number of extreme points is equal to or different from the number of zero-crossing points; (b). At any moment, the upper and lower envelopes are symmetrical to the time axis.

Further, in the step (4), h _k (t) satisfies the stopping criterion formula:

ε represents the screening threshold, which is between 0.2 and 0.3.

Preferably, the first standard value in the step (7) is -0.15, and the second standard value is 0.08.

Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

The present invention uses the extreme energy decomposition method (Extremum Energy Decomposition, EED) to analyze the RRI signal, decomposes the original signal into multiple components, that is, the extreme component function, calculates the energy of each component, and obtains its energy distribution; The signal can be decomposed into signals of different time levels from high frequency to low frequency according to the fluctuation law of the raw RRI signal, and the frequency band is divided more carefully; the data length obtained by extreme value decomposition at all levels is the same, so it will not lead to data length Reduced, so that it can be used for short-term data analysis, that is, a small amount of data is needed to analyze and get accurate results; EED is not easy to be affected by noise for different levels of component energy analysis.

Description of the drawings

Figure 1 is a schematic diagram of the original signal in the present invention;

Fig. 2 is a schematic diagram of obtaining an envelope of an original signal in the present invention;

Fig. 3 is a schematic diagram of the original signal minus the envelope mean value signal in the present invention;

Fig. 4 is a schematic diagram of the first extremum modal function component obtained in the present invention;

Figure 5 is a schematic flow chart of the extreme value energy decomposition method in the present invention;

6 is a schematic diagram of the EED decomposition of the RRI signal in Embodiment 1 of the present invention;

FIG. 7 is a schematic diagram of the normalized energy distribution in the RRI signal in Embodiment 1 of the present invention.

detailed description

The technical scheme of the present invention will be further described below in conjunction with the accompanying drawings.

As shown in Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5, a heart rate variability signal analysis method based on extreme energy decomposition method of the present invention includes the following steps:

(1) Obtain the ECG signal of the unknown state at a given time and a given sampling frequency, and then perform denoising preprocessing on the ECG signal, extract the RRI signal from it, and obtain the RRI signal x(t) of the unknown state; where denoising The specific method of preprocessing is as follows: Since the ECG energy is mainly concentrated in 0-40Hz, the ECG signal is filtered through a 40Hz zero-phase FIR low-pass filter to eliminate high-frequency noise, and then a median filter is used to remove baseline drift;

(2) Taking the RRI signal x(t) as the original signal, the minimum amount of data required for the original signal x(t) is N=2 ⁿ⁺¹ , where n is the number of decomposed extreme modal function components; find All the local extreme points of the original signal, and then all the maximum points and all the minimum points of the original signal are connected by a spline curve to form the upper envelope e _max _{and the lower envelope e min} , and the upper and lower envelopes e min are obtained. The envelope mean signal of the lower envelope m(t)=(e _max + e _min )/2;

(3) Subtract the envelope mean signal m(t) from the original signal x(t) to obtain h(t)=x(t)-m(t); then judge whether h(t) satisfies the extreme mode If the judgment condition of the function is not met, return h(t) as the original signal to step (2) until h _k (t) satisfies the judgment condition of the extreme modal function, then write c ₁ (t) = h _k (t), as the first extreme value modal function component; among them, the judging condition of the extreme value modal function is: (a). In the whole data sequence, the number of extreme points is equal to the number of zero crossing points or one difference ; (B). At any moment, the upper and lower envelopes are symmetrical to the time axis;

Where h _k (t) satisfies the stopping criterion formula:

ε represents the screening threshold, which is between 0.2 and 0.3; the extremum modal decomposition that meets the stopping criterion meets the following two conditions: (a) The final extremum modal function component c _n (t) or margin r _n (t) is less than the preset threshold; (b) the residual signal r _n (t) becomes a monotonic signal, and the extreme modal function signal cannot be extracted from it;

E _i ＝∫|c _i (t)| ² dt,i＝1,2,...,n

Normalize each energy value to get a normalized energy distribution vector

p _i ＝E _i /E,i＝1,2,...,n

among them,

(7). Select the second component p ₂ to the seventh component p ₇ and calculate the energy difference value EDV, EDV=(p ₂ +p ₃ +p ₄ )-(p ₅ +p ₆ +p ₇ ), when When EDV ≤ the first standard value, it is determined that the RRI signal is a normal heart rate variability signal. When the first standard value <EDV <the second standard value, it is determined that the RRI signal is a suspected abnormal heart rate variability signal, when EDV ≥ At the second standard value, it is determined that the RRI signal is an abnormal heart rate variability signal; the first standard value is -0.15, and the second standard value is 0.08.

The extreme energy decomposition method (Extremum Energy Decomposition, EED) adopted by the present invention is a method based on the concept of extreme value modal function, which is a type of signal with a single frequency that satisfies the following two conditions at the same time , The two conditions are:

(a) In the entire data sequence, the number of extreme points (including maximum and minimum) and the number of zero-crossing points must be equal or differ by at most one;

(b) At any time, the upper envelope formed by the local maximum point and the lower envelope formed by the local minimum point have a mean value of zero, that is to say, the local upper and lower envelopes are locally symmetric with respect to the time axis;

The above two conditions, condition (a) is similar to the requirement of Gaussian normal stationary process for traditional narrowband, condition (b) ensures that the instantaneous frequency calculated by the extreme value modal function has physical meaning.

The standard selection for the termination of the extreme value modal function decomposition of the present invention should be moderate. Too strict conditions will cause the last few extreme value modal function components to lose meaning; too loose conditions will cause useful components to be lost; in practical applications, It is also possible to set the number of extreme modal function component levels that need to be decomposed according to requirements, and the calculation is terminated when the decomposition level is satisfied.

Example 1

The EED analysis method is used to analyze the energy distribution of ECG in healthy people and CHF patients at different levels.

A heart rate variability signal analysis method based on extreme energy decomposition method for healthy people includes the following steps:

(1) Obtain the ECG signals of healthy people from physionet's RR interval database nsr2db; the data contains 54 healthy people (aged 28-76, average 61), and then denoise the ECG signals and extract RRI signals from them , The RRI signal x(t) is obtained; the specific method of denoising preprocessing is: Since the ECG energy is mainly concentrated in 0-40Hz, the ECG signal is filtered through a 40Hz zero-phase FIR low-pass filter to eliminate high-frequency noise, and then passed through the middle Value filter to remove baseline drift;

(2) Taking the RRI signal x(t) as the original signal, the minimum amount of data required for the original signal x(t) is N = 2 ⁿ⁺¹ = 2 ⁹ , where n is the number of decomposed extreme modal function components , N=8; find all the local extreme points of the original signal, and then connect all the maximum points and all the minimum points of the original signal with a spline curve to form the upper envelope e _max and the lower envelope respectively Line e _min to obtain the envelope mean signal m(t)=(e _max +e _min )/2 of the upper and lower envelopes;

(5). Subtract the first extremum modal function component c ₁ (t) from the _{original signal x(t) to get the margin r 1} (t) = x(t)-c ₁ (t), and replace r ₁ (t) as the new original sequence x(t), return to steps (2) and (3) to obtain the second, third,..., and eighth extreme modal function components and the margin r ₈ (t), Then the original signal x(t) is decomposed into 8 extreme value modal function components and a margin, namely

Obtain the extremum modal decomposition diagram of a healthy person as shown in Figure 6, and it can be seen that component 1 has the highest frequency, and the signal fluctuates on the shortest time scale. As the component number increases, the frequency gradually decreases;

(6) Perform spectrum analysis on the extreme value modal function components c _i (t), i = 1, 2, ..., 8, and obtain the center frequency of each extreme value modal function component, and obtain the frequency domain analysis result diagram;

(7). The 8 extreme modal function components decomposed from the original signal x(t) represent the components of the original signal in different frequency bands, and then the energy of each component is calculated

E _i ＝∫|c _i (t)| ² dt,i=1，2，…，8

Normalize each energy value to get a normalized energy distribution vector

p _i ＝E _i /E,i＝1,2,…,8

among them,

The first component p ₁ represents the energy of the highest frequency band, and represents the proportion of the energy distribution of the signal in the highest frequency range, and the last component p _n represents the proportion of the energy distribution of the signal in the lowest frequency band.

(8). Select the second component p ₂ to the seventh component p ₇ and calculate the energy difference value EDV, EDV=(p ₂ +p ₃ +p ₄ )-(p ₅ +p ₆ +p ₇ )=- 0.2092±0.2940.

A heart rate variability signal analysis method based on extreme energy decomposition method for CHF patients includes the following steps:

(1). Acquire ECG signals from physionet's RR interval database chf2db database. The chfdb database contains 29 congestive heart failure (CHF) patients (age 34-79, average 55), and then perform ECG signal analysis. Denoising preprocessing, extract the RRI signal from it, and get the RRI signal x(t); the specific method of denoising preprocessing is: Since the ECG energy is mainly concentrated in 0-40Hz, the ECG signal is passed through a 40Hz zero-phase FIR low-pass filter Filter to eliminate high-frequency noise, and then pass through a median filter to remove baseline drift;

(4). Subtract the first extremum modal function component c ₁ (t) from the _{original signal x(t) to obtain the margin r 1} (t) = x(t)-c ₁ (t), and replace r ₁ (t) as the new original sequence x(t), return to steps (2) and (3) to obtain the second, third,..., and eighth extreme modal function components and the margin r ₈ (t), Then the original signal x(t) is decomposed into 8 extreme value modal function components and a margin, namely

(5) Perform spectrum analysis on the extreme value modal function components c _i (t), i = 1, 2, ..., 8, and obtain the center frequency of each extreme value modal function component;

(6). The 8 extreme modal function components decomposed from the original signal x(t) represent the components of the original signal in different frequency bands, and then the energy of each component is calculated

E _i ＝∫|c _i (t)| ² dt,i=1，2，…，8

Normalize each energy value to get a normalized energy distribution vector

p _i ＝E _i /E,i＝1,2,…,8

among them,

The first component p ₁ represents the energy of the highest frequency band, which represents the proportion of the energy distribution of the signal in the highest frequency range, and the last component p _n represents the proportion of the energy distribution of the signal in the lowest frequency range; according to the return of healthy people and CHF patients A normalized energy distribution map is drawn with the unified energy distribution vector, where the abscissa represents the component level, the ordinate represents the normalized energy distribution vector value, the curve represents the average value, and the error bar represents the standard deviation;

(7). Select the second component p ₂ to the seventh component p ₇ and calculate the energy difference value EDV, EDV=(p ₂ +p ₃ +p ₄ )-(p ₅ +p ₆ +p ₇ )=0.2642 ±0.4070.

In order to further analyze the average center frequencies of the HRV signals of healthy people and CHF patients at different levels, the present invention obtains the results in Table 1.

Table 1 Average center frequencies of HRV signals of healthy people and CHF patients at different component levels

It can be concluded that the center frequency of HRV gradually decreases as the component level increases.

The typical methods of traditional Power Spectral Density (PSD) method for frequency domain segmentation are: HF (0.15 ~ 0.4 Hz), LF (0.04 ~ 0.15 Hz), VLF (0.0033 ~ 0.04 Hz). For component level 1, the frequency of the EED method of the present invention is higher than HF; level 2 is in the frequency range of HF;

levels

3 and 4 are in the range of LF; levels 5-7 are in the frequency range of VLF; and level 8 is lower than VLF. In addition, it can be seen that the frequency of CHF patients at the same level is slightly higher than that of healthy people, which reflects the influence of heart disease on HRV fluctuation rhythm. At the same level, HRV fluctuates faster in CHF patients.

In the present invention, the extreme value modal function components C _i (t) obtained by decomposing the HRV signals of the two groups are calculated to obtain the normalized energy distribution vector, and the EED curve is drawn, as shown in FIG. 7. Figure 7 is a schematic diagram of EED analysis results of RR interval signals in healthy people and CHF patients. The data length is 10000 points, the curve represents the mean value, and the error bar represents the standard deviation. The symbol * above the curve indicates that the energy T test p<0.01 of the two groups of people. In the selection of levels, levels 1 and higher than 7, including margins, are removed. Level 1 is easily affected by noise, leading to large fluctuations in energy, causing excessive standard deviation of the results, and its frequency can be as high as several kHz, so it has no clear physiological significance; levels higher than 7 reflect the long-term signal Rhythm is very susceptible to the influence of the external environment, and its frequency is very low, and its physiological significance is unknown. In Figure 7, at the low-weight level (levels 2, 3), the normalized energy value of CHF patients is significantly higher than that of healthy people, and at the high-weight level (level>5), the opposite changes occur, and the energy of healthy people is higher than that of healthy people. Energy of CHF patients; healthy people's energy is relatively stable at levels 2 to 5. When the level is greater than 5, energy rises slowly, while CHF patients decline rapidly at levels 2 to 4, and then stabilize; in 4 levels (2, 3, 6 , 7), there is a significant difference in energy between the two (p<0.01). For comparison, the present invention adds the EED analysis results of surrogate data (Healthy Surrogate, CHF Surrogate). As shown in Figure 7, the surrogate data is generated by randomizing the original data. The energy distribution of the surrogate data is monotonous as the scale increases. Decrease, compared with CHF patients, the energy is higher on a small time scale, and energy is lower on a long time scale.

In order to evaluate the energy distribution difference between the low-level and high-level decomposition of the EED curve, the energy difference value EDV of each group is calculated. A high EDV value indicates a higher component low-level energy distribution and a lower component high-level energy distribution of the RRI signal. . The EDV values of healthy people, CHF patients and their replacement data are calculated, as shown in Table 2.

Table 2 EDV values of healthy people and CHF patients

*Represents the T test results of healthy people and CHF patients p<0.0001.

**Represents the result of T test between the alternative data and its original data p<0.0001.

It can be seen from Table 2 that the EDV value of healthy people and CHF patients are significantly different, and the EDV value of CHF patients is much higher than that of healthy people. The EDV value of a healthy person is less than 0, indicating that there is higher energy in the high-level component, which indicates that the high-level component has a higher adjustment intensity. The EDV values of the two populations are significantly different from their surrogate data EDV values, and the EDV of human HRV is significantly smaller than the random sequence.

Heart rate variability (HRV) refers to the measurement of the time variability between consecutive cardiac cycles, to be precise, it should be the measurement of the variance of the difference between the normal P-P intervals that occur continuously. However, because the P wave is not as obvious as the R wave or the top of the P wave is sometimes wide and blunt, the research on the heart rate variability signal is usually replaced by the R-R interval signal (RRI) equal to the P-P interval. Studies have shown that HRV can be used as a non-invasive detection indicator of autonomic nervous system activity, especially in judging the prognosis of certain cardiovascular diseases.

In clinical and practical applications, the present invention proposes that 512 (n=8) continuous RR intervals of short-term heartbeat signals can be used for the above-mentioned EED analysis to be effective, and there is a certain amount of data margin; from the above-mentioned research It shows that the EED decomposition reaches 7 component levels (n=7) with good results, and the required amount of data can be at least N=2 ⁿ⁺¹ =2 ⁷⁺¹ =256.

Claims

A heart rate variability signal analysis method based on extreme energy decomposition method is characterized in that it comprises the following steps:

(1) Obtain the ECG signal of the unknown state at a given time and a given sampling frequency, and then perform denoising preprocessing on the ECG signal, extract the RRI signal from it, and obtain the RRI signal x(t) of the unknown state;

(2) Take the RRI signal x(t) as the original signal, find all the local extreme points of the original signal, and then connect all the maximum points and all the minimum points of the original signal using spline curves to form them respectively The upper envelope e max and the lower envelope e min are used to obtain the envelope mean signal m(t)=(e max +e min )/2 of the upper and lower envelopes;

(3) Subtract the envelope mean signal m(t) from the original signal x(t) to obtain h(t)=x(t)-m(t); then judge whether h(t) satisfies the extreme mode If the judgment condition of the function is not met, return h(t) as the original signal to step (3) until h k (t) meets the judgment condition of the extreme modal function, then write c 1 (t) = h k (t), as the first extreme value modal function component;

(4) Subtract the first extremum modal function component c 1 (t) from the original signal x(t) to obtain the margin r 1 (t) = x(t)-c 1 (t), and then judge Whether h k (t) meets the stopping criterion, if not, take r 1 (t) as the new original sequence x(t), and return to steps (2) and (3); if h k (t) meets the stopping criterion But when n<8, return to step (1) to obtain the original signal again; if h k (t) meets the stopping criterion and n≥8, the second, third, ..., nth extreme modal function components and margins are obtained r n (t), then the original signal x(t) is decomposed into n extreme value modal function components and a margin, namely

(5) Perform frequency spectrum analysis on the extreme value modal function components c i (t), i=1, 2,..., n to obtain the center frequency of each extreme value modal function component;

(6). The n extreme value modal function components decomposed from the original signal x(t) represent the components of the original signal in different frequency bands, and then the energy of each component is calculated

E i ＝∫|c i (t)| 2 dt,i＝1,2,...,n

Normalize each energy value to get a normalized energy distribution vector

p i ＝E i /E,i＝1,2,...,n

among them,
The first component p 1 represents the energy of the highest frequency band, which represents the proportion of the energy distribution of the signal in the highest frequency range, and the last component p n represents the proportion of the energy distribution of the signal in the lowest frequency range; according to the normalized energy distribution vector Draw a normalized energy distribution map, where the abscissa represents the component level, the ordinate represents the normalized energy distribution vector value, the curve represents the average value, and the error bar represents the standard deviation;

(7). Select the second component p 2 to the seventh component p 7 and calculate the energy difference value EDV, EDV=(p 2 +p 3 +p 4 )-(p 5 +p 6 +p 7 ), when When EDV ≤ the first standard value, it is determined that the RRI signal is a normal heart rate variability signal. When the first standard value <EDV <the second standard value, it is determined that the RRI signal is a suspected abnormal heart rate variability signal, when EDV ≥ At the second standard value, it is determined that the RRI signal is an abnormal heart rate variability signal.
A heart rate variability signal analysis method based on the extreme value energy decomposition method according to claim 1, wherein the minimum amount of data required for the original signal x(t) is N=2 n+1 , where n is the decomposition The number of extreme value modal function components.
A heart rate variability signal analysis method based on extreme energy decomposition method according to claim 1, characterized in that: the specific method of denoising preprocessing in the step (1) is: passing the ECG signal through 40Hz zero phase FIR low The pass filter removes high frequency noise, and then passes through the median filter to remove baseline drift.
A heart rate variability signal analysis method based on the extreme value energy decomposition method according to claim 1, characterized in that: the determination condition of the extreme value modal function in the step (3) is: (a), in the entire data sequence In, the number of extreme points is equal to or one difference from the number of zero-crossing points; (b). At any moment, the upper and lower envelopes are symmetrical with respect to the time axis.
A heart rate variability signal analysis method based on extreme energy decomposition method according to claim 1, characterized in that: in the step (4), h k (t) satisfies the stopping criterion formula:

ε represents the screening threshold, which is between 0.2 and 0.3.
A heart rate variability signal analysis method based on extreme energy decomposition method according to claim 1, wherein the first standard value in the step (7) is -0.15, and the second standard value is 0.08.