Electrocardiosignal analysis method and device

Abstract

The invention provides an electrocardiosignal analysis method which comprises the following steps: preprocessing electrocardiosignals of a subject to obtain preprocessed electrocardiosignals; taking 0.04-0.4 Hz as a full frequency band and 0.15-0.4 Hz as a high frequency band, performing bispectrum analysis according to the preprocessed electrocardiosignal, and calculating a high-low synchronization ratio index synchHigh Low; the synchHighLow is equal to # imgabs0 #, and the synchHighLow is equal to # imgabs0 #; wherein the # imgabs 1 # is the coupling energy of the preprocessed electrocardiosignal at the high frequency band; # imgabs2 is the coupling energy of the preprocessed electrocardiosignal in the full frequency band. The synchHighLow reflects the relative magnitude of the coupling energy of the high frequency band and the full frequency band through the logarithmic form of the ratio, migration of the coupling energy of the electrocardiosignal between the high frequency and the low frequency can be visually shown, the activity changes of the high frequency and the low frequency are revealed, and the evaluation of the cardiac autonomic nerve balance state is facilitated.

Description

Technical Field

[0001] This application relates to the analysis technology of electrocardiogram (ECG) signals, and particularly to an ECG signal analysis method and its device. Background Art

[0002] In the field of ECG signal analysis, the estimation of power spectrum density (PSD) based on the RR interval sequence is very common. The LF / HF index extracted from PSD belongs to the heart rate variability index and is an important index reflecting the balance of the cardiac autonomic nerves. When performing PSD analysis, the low-frequency band (LF) is usually set to 0.04 - 0.15 Hz, and the high-frequency band (HF) is set to 0.15 - 0.4 Hz to obtain the power of the LF and HF bands and the ratio LF / HF between the two. Currently, it is generally believed that the LF power reflects the joint regulation of the sympathetic and vagus nerves on the heart rate and is related to the baroreceptor reflex of blood pressure; the HF power is related to the efferent activity of the vagus nerve and is mainly affected by respiratory activity; the LF / HF index can reflect the proportion of high-frequency and low-frequency energies, thereby inferring the balance state of the cardiac autonomic nerves.

[0003] The power spectrum is called the second-order spectrum, which is the Fourier transform of the autocorrelation function of the signal. It is the one-dimensional Fourier transform of the second-order cumulant, and the power spectrum can only reflect the amplitude information at each frequency. The following is a typical application example of PSD estimation in ECG signal analysis: (1) Data preprocessing: Input the ECG signal for processing such as denoising and baseline drift removal; (2) Perform QRS complex detection and extract the RR interval sequence; (3) Perform ectopic beat detection, substitution, and detrending processing on the RR interval sequence; (4) Select a PSD estimation method, such as the AR model method, to calculate the PSD; (5) Divide the low-frequency band (LF) into 0.04 - 0.15 Hz and the high-frequency band (HF) into 0.15 - 0.4 Hz, and calculate the power of each band and LF / HF; (6) For each ECG data, repeat the above five steps, and respectively statistically analyze whether there are significant differences in the average levels of high-frequency and low-frequency powers and LF / HF between the healthy group and the disease group, and draw a conclusion on the autonomic nerve function state of the disease group.

[0004] When processing and analyzing electrocardiogram (ECG) signals, people are accustomed to assuming that the signals or noises follow a Gaussian distribution. However, in reality, most real signals are non-Gaussian. Traditional power spectrum analysis methods lose some useful information of non-Gaussian processes. When actually processing signals, power spectrum analysis has the following several disadvantages: 1) Loss of phase information. The LF / HF index only focuses on the ratio of low-frequency and high-frequency powers and only reflects the energy distribution of the signal in different frequency bands. For changes in the phase relationship of cardiac electrical activities, power spectrum indices are difficult to detect; 2) Inability to obtain non-Gaussian characteristics. The ECG signal is essentially a non-Gaussian signal, and power spectrum indices cannot extract the non-Gaussian information of the signal, which is not conducive to accurately judging the true electrical activity state of the heart; 3) Weak ability to resist Gaussian noise. During the actual acquisition of ECG signals, noise interference is often accompanied. Power spectrum analysis cannot suppress Gaussian noise, and the accuracy of its calculation results is easily significantly affected by Gaussian noise, resulting in deviations in the assessment of the heart state; 4) Inability to obtain non-linear characteristics. Power spectrum analysis belongs to a linear analysis method and cannot extract the non-linear characteristics of the signal. For non-linear systems, the power spectrum cannot provide the non-linear dynamic characteristics of the signal. Summary of the Invention

[0005] In view of the above problems, the present application aims to propose an ECG signal processing method to capture the non-linear characteristics and phase coupling information of the signal. The present application also aims to propose an ECG signal processing method to reflect the balance state of the autonomic nerves through the analysis results.

[0006] The ECG signal analysis method of the present application includes: Preprocess the ECG signal of the subject to obtain the preprocessed ECG signal; take 0.15 - 0.4 Hz as the high-frequency band and 0.04 - 0.4 Hz as the full frequency band, and perform bispectrum analysis based on the preprocessed ECG signal to calculate the high-low synchronization ratio - synchHighLow; synchHighLow = ; where is the coupling energy of the preprocessed ECG signal in the high-frequency band; is the coupling energy of the preprocessed ECG signal in the full frequency band.

[0007] Preferably, synchHighLow is used as an evaluation index for the balance state of the autonomic nerves.

[0008] Preferably, the preprocessing includes: denoising, QRS wave detection, ectopic beat detection, ectopic beat replacement, detrending.

[0009] Preferably, the denoising includes wavelet or adaptive denoising.

[0010] Preferably, the QRS wave detection includes wavelet or differential threshold QRS wave detection.

[0011] Preferably, the ectopic beat detection includes percentage filtering, median filtering, or standard deviation filtering.

[0012] Preferably, the ectopic beat replacement includes mean replacement, median replacement, or difference interpolation replacement.

[0013] Preferably, the detrending includes wavelet detrending or smoothing detrending.

[0014] The electrocardiogram signal analysis device of the present application includes: A preprocessing unit that preprocesses the input electrocardiogram signal to obtain a preprocessed electrocardiogram signal; A synchHighLow calculation unit that performs bispectrum analysis on the preprocessed electrocardiogram signal with 0.15 - 0.4 Hz as the high-frequency band and 0.04 - 0.4 Hz as the full-frequency band according to synchHighLow = to calculate synchHighLow; where is the coupling energy of the preprocessed electrocardiogram signal in the high-frequency band; is the coupling energy of the preprocessed electrocardiogram signal in the full-frequency band.

[0015] The present application modifies the frequency band of the bispectrum synchFastSlow index to a frequency band suitable for electrocardiogram analysis, and innovatively obtains the bispectrum high-low synchronization ratio index synchHighLow. synchHighLow reflects the relative magnitude of the coupling energy between the high-frequency and full-frequency bands through the logarithmic form of the ratio, can intuitively display the migration of the coupling energy of the electrocardiogram signal between the high and low frequencies, and reveal the activity changes of the high and low frequencies. This index has the advantages of bispectrum analysis, can extract the non-linear coupling information of the high and low frequencies of the electrocardiogram signal, and is also helpful for evaluating the autonomic nerve balance state, providing a key reference for disease detection and prognosis evaluation. Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the bispectrum scope.

[0017] Figure 2 It is a schematic diagram of the difference between the power spectrum and the bispectrum during phase detection.

[0018] Figure 3 It is the application flow chart of the bispectrum synchHighLow in the present application.

[0019] Figure 4 It is the two-dimensional bispectrum diagram (left) and three-dimensional bispectrum diagram (right) of a healthy person.

[0020] Figure 5 For the two-dimensional bispectrum diagram (left) and three-dimensional bispectrum diagram (right) of end-stage renal disease patients.

[0021] Figure 6 For the scatter plot of synchHighLow vs. LF / HF based on 2h of daytime and nighttime electrocardiogram data.

[0022] Figure 7 For the scatter plot of synchHighLow vs. LF / HF based on 5min of electrocardiogram data.

[0023] Figure 8 For the schematic diagram of the statistical results of synchHighLow in healthy people, patients with chest pain due to acute coronary syndrome, and patients with acute myocardial infarction.

[0024] Figure 9a For the ROC curve graph of synchHighLow in differentiating healthy people and patients with chest pain due to acute coronary syndrome.

[0025] Figure 9b For the ROC curve graph of synchHighLow in differentiating healthy people and patients with acute myocardial infarction.

[0026] Figure 10a For the schematic diagram of the statistical results of synchHighLow in healthy people and end-stage renal disease patients.

[0027] Figure 10b For the ROC curve graph of synchHighLow in differentiating healthy people and end-stage renal disease patients. Detailed implementation manners

[0028] Next, the present application will be described in detail with reference to the accompanying drawings.

[0029] The bispectrum has symmetry and periodicity Regarding Symmetry: Regarding Or Symmetry: This is due to the periodicity of the Fourier transform, from which the periodicity of the bispectrum can be known: The original scope of the bispectrum is , and from the symmetry and periodicity of the above bispectrum, it can be obtained that this scope is symmetric about , , ， Symmetric, with respect to Periodically repeated, such as Figure 1 As shown, the original scope is the entire large square, which is divided into sixteen parts by the above-mentioned axis of symmetry. That is, as long as the bispectral information in the original scope is one-sixteenth of the original, it can be fully included. In this way, the computational complexity of the algorithm is reduced to one-sixteenth of the original.

[0030] The bispectrum can detect phase coupling information Consider two signals: It can be found that for and in terms of their power spectra are exactly the same, while in the bispectrum, the bispectrum of is 0, the bispectrum of shows a pulse at (1, 2), that is, there is a quadratic phase coupling (see Figure 2 ). This shows that the bispectrum can discover phase coupling information that cannot be found in the power spectrum.

[0031] The electrocardiogram signal analysis method of this application, referring to the remarkable success of the synchFastSlow index of bispectral analysis in anesthesia depth monitoring, innovatively changes the synchFastSlow of bispectral analysis to the synchHighLow of high-low synchronization ratio suitable for electrocardiogram signal analysis.

[0032] The bispectrum is also called the third-order spectrum. The third-order spectrum is the two-dimensional Fourier transform of the third-order autocorrelation function of the signal. As the simplest and most practical high-order spectrum, the bispectrum is simple to calculate and has all the advantages of high-order spectrum analysis. The bispectrum is a two-dimensional complex spectrum, and its peak represents the phase coupling strength of these two frequency components of the signal, characterizing the non-Gaussian characteristics of the signal.

[0033] The definition of the third-order spectrum can be further deduced from the definition of the second-order statistic and the second-order spectrum. For the zero-mean stationary process X(n), first obtain the third-order statistic (third-order autocorrelation function): The third-order spectrum is the two-dimensional Fourier transform of the third-order statistic: Where: , When the signal is a finite-length sequence, through derivation, the bispectrum estimate can be obtained: Where is a finite-length discrete random signal of length N Fourier transform of: One of the most significant applications of bispectral analysis is the bispectral index (BIS) for analyzing the depth of anesthesia. BIS is currently the most widely used index for evaluating the depth of anesthesia and sedation in clinical practice. synchFastSlow is a BIS subparameter applied to detect the depth of anesthesia. It can reflect the nonlinear synchronization (non-Gaussian coupling) of fast and slow frequency band neural oscillations, thereby providing information on the depth of anesthesia. In the BIS index, synchFastSlow is defined as: where, In electroencephalogram (EEG) analysis, usually the representing the entire frequency band (full frequency band) is set to 0.5 - 47 Hz, and the representing the high frequency band is set to 40 - 47 Hz.

[0034] In the present invention, by changing the frequency bands in bispectral synchFastSlow to the full frequency band (LF1 - HF2 set to 0.04 - 0.4 Hz) and high frequency band (HF1 - HF2 set to 0.15 - 0.4 Hz) suitable for electrocardiogram (ECG) analysis, a new bispectral high - low synchronization ratio index - synchHighLow is obtained.

[0035] The definition of synchHighLow is: synchHighLow = The meaning of synchHighLow is the logarithm of the ratio of the coupling energy in the high frequency band to the coupling energy in the full frequency band. From the definition, it can be seen that when synchHighLow increases, it indicates that the coupling energy of the ECG signal migrates from low frequency to high frequency, that is, the high frequency activity increases; when synchHighLow decreases, it indicates that the coupling energy of the ECG signal migrates from high frequency to low frequency, that is, the low frequency activity increases.

[0036] synchHighLow has all the advantages of bispectral indexes, can extract the high - low frequency nonlinear coupling information of ECG signals, and helps to better evaluate the autonomic nerve state. Introducing bispectral - related indexes in ECG signal analysis is beneficial to extracting richer and more reliable information, providing important references for the detection and prognosis evaluation of diseases.

[0037] To measure the effect of the present invention, the correlation analysis of synchHighLow and LF / HF, the difference analysis of synchHighLow between healthy people and patient populations, and the effectiveness analysis of synchHighLow in detecting autonomic neuropathy were carried out using healthy people and three disease populations respectively.

[0038] Data The experimental data were from THEW database (Telemetric and Holter ECG Warehouse, HYPERLINK"http: / / www.thew-project.org" http: / / www.thew-project.org), which included 24-hour Holter electrocardiogram data of various populations. Four sub-databases were selected from them. One was the Normal sub-database (E-HOL-03-0202-003), which included electrocardiogram data of 202 normal people; the second was the AMI sub-database (E-HOL-03-0160-001), which included electrocardiogram data of 93 patients with acute myocardial infarction (AMI); the third was the CPP sub-database (E-HOL-12-1172-012), which included electrocardiogram data of 802 patients with chest pain of acute coronary syndrome (CPP); the fourth was the ESRD sub-database (E-HOL-12-0051-016). The selected individuals were all patients with end-stage renal disease (ESRD) who were receiving hemodialysis treatment. They suffered from hypertension or diabetes at the same time and were high-risk populations with abnormal autonomic function.

[0039] Correlation analysis of synchHighLow and LF / HF The Normal sub-database representing the normal population was selected. All 24-hour Holter electrocardiogram records were manually analyzed through Kubios software. Two-hour relatively quiet data segments were manually selected from daytime (07:00–20:00) and nighttime (20:00-07:00) for analysis. In addition, a 5-minute data segment without movement or napping was extracted from each electrocardiogram record (between 8 am and 5 pm). The correlation analysis of synchHighLow and LF / HF was carried out from two perspectives: short-term and long-term, daytime and nighttime.

[0040] Calculation of synchHighLow: (1) For a certain electrocardiogram data, preprocessing operations were carried out, including filtering, denoising, and baseline correction, etc.; (2) QRS wave detection, and the RR interval was obtained by differential calculation; (3)RR interval ectopic detection, replacement, and detrending; (4)Divide the two main frequency bands in the bispectral domain: the full frequency band LF1 - HF2 is defined as 0.04 - 0.4 Hz, and the high frequency band HF1 - HF2 is defined as 0.15 - 0.4 Hz, and calculate synchHighLow; (5)Repeat the above four steps to obtain synchHighLow for all individuals.

[0041] (6)Statistical analysis of the correlation, and the results are as Figure 6 shown. The left is the data result during the day, and the right is the data result at night.

[0042] In the 2 - hour daytime and nighttime electrocardiogram data, the scatter plot of synchHighLow and LF / HF is as Figure 6 shown. It can be seen that synchHighLow and LF / HF generally show a linear correlation trend. Spearman correlation analysis shows that the correlation coefficients of the two are -0.766 ( p p = 0.00) and -0.797 ( p p = 0.00), indicating that synchHighLow and LF / HF have a strong correlation. Based on the 5 - minute electrocardiogram data, synchHighLow and LF / HF also show a linear correlation trend (see Figure 7 ), and the Spearman correlation coefficient is -0.883 ( p p = 0.00), indicating that synchHighLow and LF / HF have a strong correlation. It can be seen that regardless of whether it is 2 - hour or 5 - minute data, and regardless of whether it is daytime or nighttime data, synchHighLow and LF / HF show a significant strong correlation (especially for short - term data). Therefore, synchHighLow is very likely to be a supplementary index that can reflect the autonomic nerve balance state in addition to LF / HF.

[0043] Analysis of the difference in synchHighLow between healthy people and patient populations Select Normal as the control group, and the CPP and AMI sub - databases represent the experimental groups. For each record, 5 - minute time - segment data is selected for analysis.

[0044] The synchHighLow levels of each group are represented by the median and inter - quartile range. The statistical results show that compared with the healthy control group (-2.48, 0.95), the synchHighLow values of CPP patients (-2.14, 0.96) are significantly increased ( p p = 0.00), and the synchHighLow values of AMI patients (-2.11, 0.86) are also significantly increased ( p= 0.00), there was no significant difference in synchHighLow between the CPP and AMI pathological groups (see Figure 8 ). This result proves that pathological factors can lead to enhanced signal synchronization and increased coupling strength in the high-frequency band, and thus the synchHighLow of patients shows a significant increase compared to the normal value.

[0045] ROC analysis showed (see Figure 9a , 9b ) that the AUC of synchHighLow for distinguishing CPP patients from healthy people was 0.667, while the AUC of LF / HF was 0.619; the AUC of synchHighLow for distinguishing AMI patients from healthy people was 0.672, while the AUC of LF / HF was 0.648. It can be seen that the ability of synchHighLow to distinguish the disease group from normal people is significantly better than that of LF / HF, and synchHighLow shows potential in disease detection.

[0046] Analysis of the effectiveness of synchHighLow in detecting autonomic neuropathy Normal was selected as the control group, and the ESRD sub-database represented the pathological group of autonomic neuropathy. After removing the records with incomplete diurnal data, 189 records from Normal and 43 records from ESRD were selected respectively. Manually select a 2-hour relatively quiet data segment from daytime (07:00–20:00) for analysis.

[0047] The ability of synchHighLow to distinguish ESRD patients from healthy people was evaluated by ROC curve analysis, and performance indicators such as the area under the curve (AUC) and sensitivity were obtained, and its performance was compared with that of LF / HF. In addition, in order to verify whether the combination of synchHighLow and LF / HF can improve the ability of a single index to detect autonomic neuropathy, binary logistic regression analysis was used to obtain the prediction probabilities of synchHighLow and LF / HF indicators, and then ROC analysis was performed on the prediction probabilities to observe whether the disease detection performance was improved.

[0048] The results showed (see Figure 10a , 10b ) that compared with the healthy control group (-2.44, 0.81), the synchHighLow value of ESRD patients (-1.70, 0.74) was significantly increased ( p(<0.05), indicating that autonomic neuropathy leads to enhanced signal synchronization and increased coupling strength in the high-frequency band. The results of ROC analysis showed that the AUC of synchHighLow for distinguishing healthy individuals from ESRD was 0.828, which was much larger than the AUC of LF / HF (0.743), indicating that synchHighLow has a stronger ability to detect autonomic neuropathy than LF / HF. When -2.346 was selected as the threshold (the maximum Youden index), the diagnostic sensitivity reached 0.977. The AUC of the predicted probabilities of synchHighLow and LF / HF was 0.839, which was larger than the AUC of either synchHighLow or LF / HF alone, indicating that the combined prediction model of synchHighLow and LF / HF can better detect autonomic neuropathy. synchHighLow provides useful information not included in LF / HF, and the combined use of the two is likely to show excellent effects in the detection and prognosis assessment of autonomic diseases.

Claims

1. A method for analyzing an electrocardiogram signal, comprising: Preprocessing the subject's electrocardiogram signal to obtain a preprocessed electrocardiogram signal; Taking 0.15-0.4 Hz as the high frequency band and 0.04-0.4 Hz as the full frequency band, bispectral analysis was performed based on the preprocessed ECG signal to calculate synchHighLow; synchHighLow = ; in, is the coupling energy of the preprocessed ECG signal in the high frequency band; is the coupling energy of the preprocessed ECG signal in the full frequency band.

2. The electrocardiogram signal analysis method according to claim 1, characterized in that: synchHighLow is an evaluation index for the balance state of cardiac autonomic nerves.

3. The electrocardiogram signal analysis method according to claim 1, characterized in that: The preprocessing includes: denoising, QRS wave detection, ectopic heart beat detection, ectopic heart beat replacement, and detrending.

4. The electrocardiogram signal analysis method according to claim 3, characterized in that: The denoising includes wavelet denoising or adaptive denoising.

5. The electrocardiogram signal analysis method according to claim 3, characterized in that: The QRS wave detection includes wavelet or differential threshold QRS wave detection.

6. The electrocardiogram signal analysis method according to claim 3, characterized in that: The ectopic heart beat detection includes percentage filtering, median filtering, or standard deviation filtering.

7. The electrocardiogram signal analysis method according to claim 3, characterized in that: The ectopic heartbeat substitution includes mean value substitution, median substitution, or difference interpolation substitution.

8. The electrocardiogram signal analysis method according to claim 3, characterized in that: The detrending includes wavelet detrending or smoothing detrending.

9. An electrocardiogram signal analysis device, comprising: A preprocessing unit, which preprocesses the input ECG signal to obtain a preprocessed ECG signal; synchHighLow calculation unit, which is based on synchHighLow = , taking 0.15-0.4 Hz as the high frequency band and 0.04-0.4 Hz as the full frequency band, bispectral analysis is performed on the preprocessed ECG signal to calculate synchHighLow; in, is the coupling energy of the preprocessed ECG signal in the high frequency band; is the coupling energy of the preprocessed ECG signal in the full frequency band.

10. The electrocardiogram signal analysis device according to claim 9, characterized in that: synchHighLow is an evaluation index for the balance state of cardiac autonomic nerves.

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