CN113633293A - Heart-derived sudden death early warning method for chaotically detecting T-wave electricity alternation - Google Patents

Heart-derived sudden death early warning method for chaotically detecting T-wave electricity alternation Download PDF

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CN113633293A
CN113633293A CN202110861353.0A CN202110861353A CN113633293A CN 113633293 A CN113633293 A CN 113633293A CN 202110861353 A CN202110861353 A CN 202110861353A CN 113633293 A CN113633293 A CN 113633293A
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CN113633293B (en
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陈丹凤
黎俊生
蔡瑜萍
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Abstract

The invention discloses a chaotic detection T-wave electricity alternate sudden cardiac death early warning method, which comprises the following steps of: acquiring electrocardiogram data; removing interference noise from the electrocardiographic data; calculating T-wave electricity alternation data of the electrocardiogram data by using a chaos detection method to obtain a correlation dimension and a maximum Lyapunov index; calculating to obtain a first error and a second error, wherein the first error is an absolute value of a difference value between the correlation dimension and an average value of the correlation dimension, and the second error is an absolute value of a difference value between the maximum Lyapunov exponent and an average value of the maximum Lyapunov exponent; judging whether the first error is smaller than a first threshold value or not, and judging whether the second error is smaller than a second threshold value or not; and when the first error is smaller than a first threshold value or the second error is smaller than a second threshold value, sending a sudden cardiac death early warning. The invention can search the chaotic characteristic with higher feasibility for detecting the TWA, effectively detect the TWA and better early warn the sudden cardiac death.

Description

Heart-derived sudden death early warning method for chaotically detecting T-wave electricity alternation
Technical Field
The invention relates to the technical field of biomedicine, in particular to a sudden cardiac death early warning method for chaotically detecting T-wave electricity alternation.
Background
Sudden Cardiac Death (SCD) is defined as a loss of consciousness within up to one hour after the onset of symptoms without any possible fatal prerequisite, with the major clinical manifestations of sudden loss of consciousness, rapid, fatal, non-traumatic physiological death, and extremely low rates of hospitalization. In recent years, the cases of the death of the famous people due to the ineffective rescue of the sudden cardiac death occur frequently, the sudden cardiac death is no longer exclusive to the old at present, the sign of the aging gradually appears, and many young people after 90 years are hard to avoid the death, which obviously sounds an alarm clock for the human beings. Sudden cardiac death is caused in an acute way, and once the sudden cardiac death occurs, patients often cannot be rescued in time and lose lives. At this time, if the symptom of sudden cardiac death can be captured in advance, sufficient time can be won for the subsequent treatment of the patient, the situation that rescue is not timely caused by sudden cardiac death is avoided as much as possible, the survival rate of the patient is effectively improved, and therefore the early occurrence of the sudden cardiac death risk is required to be unbearable.
Sudden cardiac death is caused in an acute way, and once the sudden cardiac death occurs, patients often cannot be rescued in time and lose lives. At the moment, if the symptom of sudden cardiac death can be captured in advance, sufficient time can be won for subsequent treatment of the patient, the situation that rescue is not timely caused by sudden cardiac death is avoided as much as possible, and the survival rate of the patient is effectively improved. According to relevant literature data, T Wave Alternans (TWA) is pathological heart electrical activity, and the TWA is used as a tool for predicting sudden cardiac death and has high research value and wide application prospect.
Disclosure of Invention
The present invention is directed to at least solving the problems of the prior art. Therefore, the invention provides a chaotic characteristic with high feasibility for TWA detection, and the chaotic characteristic can be used for effectively detecting the TWA, so that the sudden cardiac death can be well warned.
The invention also provides a sudden cardiac death early warning system for chaotically detecting T-wave electricity alternation, which has the early warning method for chaotically detecting T-wave electricity alternation.
The invention also provides a computer readable storage medium.
In a first aspect, the embodiment provides a sudden cardiac death early warning method based on chaos detection of T-wave power alternation, which includes the following steps:
acquiring electrocardiogram data;
interference noise removal is carried out on the electrocardio data;
calculating T-wave electricity alternation data of the electrocardiogram data by using a chaos detection method to obtain a correlation dimension and a maximum Lyapunov index;
calculating to obtain a first error and a second error, wherein the first error is an absolute value of a difference value between the correlation dimension and an average value of the correlation dimension, and the second error is an absolute value of a difference value between the maximum Lyapunov exponent and an average value of the maximum Lyapunov exponent;
judging whether the first error is smaller than a first threshold value or not, and judging whether the second error is smaller than a second threshold value or not;
and when the first error is smaller than a first threshold value or the second error is smaller than a second threshold value, sending a sudden cardiac death early warning.
The early warning method for sudden cardiac death by chaotically detecting T-wave electricity alternation according to the embodiment of the invention at least has the following beneficial effects:
firstly, acquiring electrocardio data, and then preprocessing the electrocardio data to remove interference noise, wherein the electrocardio data mainly comprises myoelectric interference, power frequency interference and baseline drift, the myoelectric signal is an almost unavoidable interference signal and is caused by human body activity and tension and vibration of muscles at other parts, the interference caused by muscle twitch of an electrode plate area is irregular when the electrocardio data is acquired, the electromyogram is Gaussian white noise and belongs to high-frequency interference, and the frequency is generally distributed between 30Hz and 2000Hz and is reflected on an electrocardio waveform, so that the change rate is high, and the small irregular ripple waves are provided. The power frequency interference is caused by a power supply connected with an electrocardio acquisition device and an external electromagnetic field, belongs to alternating current signals, and is mainly embodied in that an obvious sine wave superposition is formed on an electrocardiogram, and a bit is that a plurality of fine burrs are formed on an electrocardiogram waveform. The baseline drift is generally caused by human respiration, small displacement of electrode pads of the electrocardiograph, skin surface impedance and the like. Baseline wander can cause the signal data segment to float or distort, more slowly, with a lower frequency, typically less than 1Hz, along with the baseline up and down. The interference has a great influence on subsequent electrocardio analysis research, and particularly has great influence on the accuracy of ST segment identification. Since T-wave electrical alternans appear in the ST-band on the electrocardiographic waveform, baseline wander must be dealt with as much as possible, reducing the impact on TWA detection.
Then calculating T wave electricity alternation data of the electrocardiogram data by using a chaos detection method to obtain a correlation dimension and a maximum Lyapunov index; calculating to obtain a first error and a second error, wherein the first error is an absolute value of a difference value between the correlation dimension and an average value of the correlation dimension, and the second error is an absolute value of a difference value between the maximum Lyapunov exponent and an average value of the maximum Lyapunov exponent; judging whether the first error is smaller than a first threshold value or not, and judging whether the second error is smaller than a second threshold value or not; and when the first error is smaller than a first threshold value or the second error is smaller than a second threshold value, sending a sudden cardiac death early warning. The chaos feature with high feasibility for detecting the TWA can be found, the TWA can be effectively detected, and the sudden cardiac death can be well warned.
According to some embodiments of the invention, before the preprocessing the electrocardiographic data to remove the interference noise, the method further comprises the following steps:
selecting leads and sampling time points of the electrocardiogram data;
and carrying out visual processing on the electrocardiogram data.
According to some embodiments of the invention, the preprocessing the electrocardiographic data to remove interference noise comprises the steps of;
and preprocessing the electrocardio data to remove myoelectric interference and power frequency interference and correct baseline drift.
According to some embodiments of the invention, the preprocessing of the electrocardiographic data to remove electromyographic interference, power frequency interference and baseline wander comprises the steps of;
filtering the electromyographic interference by using a Butterworth low-pass filter;
filtering the power frequency interference by using a 50Hz or 60Hz power frequency trap;
the baseline wander is corrected using a median filtered Kaiser window function.
According to some embodiments of the present invention, before the calculating the correlation dimension and the maximum Lyapunov index of the T-wave and electric alternation data of the electrocardiographic data by using a chaos detection method, the method further includes the steps of:
and selecting a power spectrum, and performing phase space reconstruction on the time sequence.
According to some embodiments of the invention, after determining whether the first error is less than a first threshold and determining whether the second error is less than a second threshold, the method comprises:
and when the first error is larger than a first threshold value and the second error is larger than a second threshold value, ending the process.
In a second aspect, the present embodiment provides a sudden cardiac death early warning system based on chaos detection and T-wave power alternation, including: the early warning method for sudden cardiac death based on chaos detection and T-wave electricity alternation comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor implements the early warning method for sudden cardiac death based on chaos detection and T-wave electricity alternation according to the first aspect when executing the computer program.
The chaotically-detected T-wave-electricity alternative sudden cardiac death early warning system provided by the embodiment of the invention at least has the following beneficial effects: the chaotically-detected T-wave electricity alternating sudden cardiac death early warning system applies the chaotically-detected T-wave electricity alternating sudden cardiac death early warning method in the first aspect, can search for chaos characteristics with high TWA detection feasibility, effectively detect TWA, and achieve good early warning on sudden cardiac death.
In a third aspect, the present embodiment provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the method for early warning of sudden cardiac death by chaotically detecting T-wave electrical alternans according to the first aspect.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which the abstract is to be fully consistent with one of the figures of the specification:
fig. 1 is a flowchart of a method for early warning sudden cardiac death by chaotically detecting T-wave power alternation according to an embodiment of the present invention;
FIG. 2 is a TWA electrocardiogram of a chaotic detection T-wave electric alternating sudden cardiac death warning method according to another embodiment of the present invention;
fig. 3 is a result diagram of S-T waves obtained after P-waves are removed by filtering in the chaos detection T-wave-electric alternating sudden cardiac death early warning method according to another embodiment of the present invention;
fig. 4 is a result diagram of S-T waves obtained after P-waves are removed by filtering in the chaos detection T-wave-electric alternating sudden cardiac death early warning method according to another embodiment of the present invention;
fig. 5 is a result diagram of S-T waves obtained after P-waves are removed by filtering in the chaos detection T-wave-electric alternating sudden cardiac death early warning method according to another embodiment of the present invention;
fig. 6 is a case diagram of phase space reconstruction of a chaotically detected T-wave-electricity alternating sudden cardiac death warning method according to another embodiment of the present invention;
fig. 7 is a result diagram of correlation dimension solving of TWA electrocardiographic data of the early warning method for sudden cardiac death by chaos detection of T-wave-power alternation according to another embodiment of the present invention;
fig. 8 is a structural diagram of a sudden cardiac death warning system with chaos detection and T-wave power alternation according to another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be noted that although functional block divisions are provided in the system drawings and logical orders are shown in the flowcharts, in some cases, the steps shown and described may be performed in different orders than the block divisions in the systems or in the flowcharts. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In recent years, TWA has been regarded as a predictor of SCD, and it has also been demonstrated that TWA, a non-invasive detection means, has great promise in predicting sudden cardiac death. However, TWA is an abnormal phenomenon that is difficult to observe by naked eyes in the electrocardiogram of the body surface of a human body and is easily interfered by various types of noise and even annihilated. Therefore, accurate detection of TWA in electrocardiograms is a challenging problem. TWA detection methods are classified according to the detection principle, and are roughly classified into a time domain detection method and a transform domain detection method.
The time domain detection method is mainly used for qualitatively and quantitatively analyzing signals through symbol transformation in a time domain and comparing TWA time domain diagnostic standards to judge whether TWA exists or not. There are often correlation assays. The correlation analysis method is to take electrocardiosignals in continuous cardiac cycles as objects, calculate the average amplitude of all T waves in the section of signals, and construct a correlation index by using information such as cross correlation and autocorrelation functions of the current amplitude and the average amplitude of the T waves, wherein the correlation index is used as a detection index of the TWA and is marked as an alternative correlation index.
TWA transform domain detection methods are based on short-time fourier transforms or spectral transforms. Spectral analysis (SM) is mainly to segment digitized electrocardiographic signals according to beat of each heart, regard them as a vector, and then perform spectral analysis and TWA detection on the vector signals in a certain window.
The TWA method for detecting the nonlinear principle comprises the following steps: the Laplace likelihood ratio method is to establish non-Gaussian TWA probability model and analyze TWA through the model, but the method has the defects of poor noise immunity and low robustness.
The invention provides a chaotic detection T-wave electricity alternating sudden cardiac death early warning method, which can search chaotic characteristics with high feasibility for TWA detection, effectively detect TWA and well early warn sudden cardiac death.
The embodiments of the present invention will be further explained with reference to the drawings.
Referring to fig. 1, fig. 1 is a flowchart of a sudden cardiac death early warning method for chaotically detecting T-wave electrical alternation according to an embodiment of the present invention, where the method includes, but is not limited to, step S110 to step S160.
Step S110, acquiring electrocardiogram data;
step S120, interference noise removal is carried out on the electrocardio data;
step S130, calculating T wave-electricity alternative data of the electrocardio data by using a chaos detection method to obtain a correlation dimension and a maximum Lyapunov index;
step S140, calculating to obtain a first error and a second error, wherein the first error is an absolute value of a difference value between the correlation dimension and an average value of the correlation dimension, and the second error is an absolute value of a difference value between the maximum Lyapunov exponent and an average value of the maximum Lyapunov exponent;
step S150, judging whether the first error is smaller than a first threshold value or not, and judging whether the second error is smaller than a second threshold value or not;
and step S160, when the first error is smaller than a first threshold value or the second error is smaller than a second threshold value, sending a sudden cardiac death early warning.
In one embodiment, firstly, electrocardiographic data is obtained, and then the electrocardiographic data is preprocessed to remove interference noise, wherein the interference noise mainly comprises myoelectric interference, power frequency interference and baseline drift, the myoelectric signal is an almost unavoidable interference signal and is caused by human activities and tension and vibration of muscles at other parts, the interference is mainly caused by muscle twitch of an area where an electrode slice is located when the electrocardiographic data is collected, the interference is irregular, the interference is Gaussian white noise and belongs to high-frequency interference, and the frequency is generally distributed between 30Hz and 2000Hz and is reflected on electrocardiographic waveforms, so that the change rate is fast, and the ripple is tiny and irregular. The power frequency interference is caused by a power supply connected with an electrocardio acquisition device and an external electromagnetic field, belongs to alternating current signals, and is mainly embodied in that an obvious sine wave superposition is formed on an electrocardiogram, and a bit is that a plurality of fine burrs are formed on an electrocardiogram waveform. The baseline drift is generally caused by human respiration, small displacement of electrode pads of the electrocardiograph, skin surface impedance and the like. Baseline wander can cause the signal data segment to float or distort, more slowly, with a lower frequency, typically less than 1Hz, along with the baseline up and down. The interference has a great influence on subsequent electrocardio analysis research, and particularly has great influence on the accuracy of ST segment identification. Since T-wave electrical alternans appear in the ST-band on the electrocardiographic waveform, baseline wander must be dealt with as much as possible, reducing the impact on TWA detection. Then calculating T wave electricity alternation data of the electrocardiogram data by using a chaos detection method to obtain a correlation dimension and a maximum Lyapunov index; calculating to obtain a first error and a second error, wherein the first error is an absolute value of a difference value between the correlation dimension and an average value of the correlation dimension, and the second error is an absolute value of a difference value between the maximum Lyapunov exponent and an average value of the maximum Lyapunov exponent; judging whether the first error is smaller than a first threshold value or not, and judging whether the second error is smaller than a second threshold value or not; and when the first error is smaller than a first threshold value or the second error is smaller than a second threshold value, sending a sudden cardiac death early warning.
In one embodiment, acquiring electrocardiographic data, selecting a lead and a sampling time point of the electrocardiographic data, performing visual processing on the electrocardiographic data, preprocessing the electrocardiographic data to remove interference noise, calculating T-wave electricity alternation data of the electrocardiographic data by using a chaos detection method to obtain a correlation dimension and a maximum Lyapunov index, and calculating to obtain a first error and a second error, wherein the first error is an absolute value of a difference value between the correlation dimension and an average value of the correlation dimension, and the second error is an absolute value of a difference value between the maximum Lyapunov index and an average value of the maximum Lyapunov index; judging whether the first error is smaller than a first threshold value or not, and judging whether the second error is smaller than a second threshold value or not; and when the first error is smaller than a first threshold value or the second error is smaller than a second threshold value, sending a sudden cardiac death early warning.
Referring to fig. 2 to 5, fig. 2 is a TWA electrocardiogram of a cardiac sudden death early warning method based on chaos detection of T-wave-electricity alternation according to another embodiment of the present invention, fig. 3 is a result graph of S-T wave after P-wave filtering and removing of the cardiac sudden death early warning method based on chaos detection of T-wave-electricity alternation according to another embodiment of the present invention, fig. 4 is a result graph of S-T wave after P-wave filtering and removing of the cardiac sudden death early warning method based on chaos detection of T-wave-electricity alternation according to another embodiment of the present invention, and fig. 5 is a result graph of S-T wave after P-wave filtering and removing of the cardiac sudden death early warning method based on chaos detection of T-wave-electricity alternation according to another embodiment of the present invention.
The T wave band of the electrocardiogram data is intercepted, and the T wave is an important component of the electrocardiogram and reflects the repolarization process of ventricular muscle. The T wave electricity alternation phenomenon is one of T wave abnormity, and researches show that sudden cardiac death and T wave electricity alternation are directly related, so that the possibility of sudden cardiac death can be predicted in advance by detecting the T wave electricity alternation phenomenon. In the electrocardio data, compared with the R wave, the T wave characteristics are not obvious and slightly change, and the problem of T wave abnormity can be solved by effectively detecting weak signals by using the chaotic system.
The detection of the T-wave electric alternation phenomenon firstly needs to intercept the S-T wave band. The wavelet packet algorithm is used for determining the time domain boundary of the T wave of the electrocardiogram by extracting the waveform of the frequency range in which the T wave of the electrocardiogram is positioned and extracting the T wave from the monocycle electrocardiogram.
According to the QRS complex of the electrocardiogram and the spectrogram analysis of the T wave, the bandwidth frequency of the QRS wave is concentrated at 0-38 Hz, nearly 99% of energy is accumulated, and the QRS wave peak energy is concentrated at 8-16 Hz; the T wave bandwidth is 0-8Hz, and the wave crest energy is concentrated in the frequency range of 1-8 Hz.
The wavelet packet algorithm is the popularization of wavelet decomposition, can effectively decompose signals into signals with different frequencies according to the frequency, and the signals are superposed and combined again to become the original signals. Aiming at the wave group bandwidth frequency distribution condition of the electrocardiogram, the S-T wave band is mainly distributed in the frequency range with the bandwidth of 0-8 Hz. By wavelet packet decomposition, for a sample signal with a sampling frequency of m Hz, the total signal S is represented as:
Figure BDA0003185865820000081
the decomposed signal bands are:
Figure BDA0003185865820000091
taking the European-TDatabase data set as an example, if the 10-second data length is 2500, the frequency is 250HZ, and the data is decomposed into 5 layers, the frequency band of the root node (5,0) is 0-8HZ, namely the S-T wave band.
In one embodiment, electrocardiographic data is obtained; preprocessing the electrocardio data to remove interference noise; calculating T-wave electricity alternation data of the electrocardiogram data by using a chaos detection method to obtain a correlation dimension and a maximum Lyapunov index; calculating to obtain a first error and a second error, wherein the first error is an absolute value of a difference value between the correlation dimension and an average value of the correlation dimension, and the second error is an absolute value of a difference value between the maximum Lyapunov exponent and an average value of the maximum Lyapunov exponent; judging whether the first error is smaller than a first threshold value or not, and judging whether the second error is smaller than a second threshold value or not; and when the first error is smaller than a first threshold value or the second error is smaller than a second threshold value, sending a sudden cardiac death early warning. The method comprises the steps of preprocessing electrocardio data, removing interference noise, removing electromyographic interference, removing power frequency interference and removing baseline drift, wherein the electromyographic signal is an almost unavoidable interference signal and is caused by human body activity and tension and vibration of muscles at other parts, the interference caused by muscle twitch of an electrode plate area is mainly generated when the electrocardio data is collected, the interference is irregular, the interference is Gaussian white noise and belongs to high-frequency interference, and the frequency is generally distributed between 30Hz and 2000Hz and is reflected on an electrocardio waveform, so that the change rate is high, and the small irregular ripple waves are generated. The power frequency interference is caused by a power supply connected with an electrocardio acquisition device and an external electromagnetic field, belongs to alternating current signals, and is mainly embodied in that an obvious sine wave superposition is formed on an electrocardiogram, namely, a lot of fine burrs are formed on an electrocardio waveform, the power frequency of most areas in China is generally 50Hz, and the power frequency of America is 60 Hz.
The baseline drift is generally caused by factors such as human respiration, micro displacement of electrode plates of an electrocardiograph, skin surface impedance and the like, the signal data segment and the baseline can float or distort up and down due to the baseline drift, the change is slow, the frequency is low and generally less than 1Hz, the interference has great influence on subsequent electrocardiographic analysis research, particularly great influence on the accuracy of ST segment identification, and the baseline drift is required to be processed as far as possible due to the fact that T wave electricity appears on ST wave bands on electrocardiographic waveforms alternately, and the influence on TWA detection is reduced.
It can be understood that a Butterworth (Butterworth) low-pass filter is selected for the electromyographic interference elimination, and the principle of the low-pass filter is that the frequency response decreases with the increase of the frequency, the signal inside the pass band can be made to flow to the maximum extent, and the stop band approaches to 0 more and more with the increase of the frequency. The power frequency of most areas in China is generally 50Hz, the power frequency of America is 60Hz, a power frequency trap of 50/60Hz is selected for removing power frequency interference, the trap has the advantages of distinct characteristics and low design difficulty, the calculation speed is high while the effect of removing the power frequency interference is ensured, for example, data of an international standard database has the frequency of 60Hz according to the America standard, and the stop band cut-off frequency is designed to be 59Hz and 61 Hz. The problem of baseline drift can be effectively corrected by using a median filtering Kaiser window function method.
Assuming that an electrocardiographic waveform signal is x (N), N is more than or equal to 0 and less than or equal to N-1, processing x (N) by using median filtering with a window width L being 2k +1, and extending k points at the head and the tail of the signal waveform:
Figure BDA0003185865820000101
and (3) carrying out median filtering on the processed new extension signal x (N), wherein k is less than or equal to N and less than or equal to N + k-1, and N is 0 to N-1 point by point to obtain baseline drift:
x (N) ═ med [ x (N-k), x (N-k +1), …, x (N), x (N +1), …, x (N + k) ], N ═ 0,1, …, N-1 where x (N) is the baseline drift waveform and med [ · ] is the median operator. Subtracting x (n) from the original waveform x (n) yields a signal with baseline drift filtered out:
Figure BDA0003185865820000102
referring to fig. 6, fig. 6 is a diagram of a case of phase-space reconstruction in a sudden cardiac death early warning method for chaotically detecting T-wave electricity alternation according to another embodiment of the present invention; fig. 7 is a result diagram of correlation dimension solving of TWA electrocardiographic data of the early warning method for sudden cardiac death by chaos detection of T-wave-power alternation according to another embodiment of the present invention.
In an embodiment, before the correlation dimension and the maximum Lyapunov index are calculated for the T-wave-electricity alternation data of the electrocardiogram data by using a chaos detection method, the method further includes the steps of: and selecting a power spectrum, and performing phase space reconstruction on the time sequence.
Phase space reconstruction: in order to expand the hidden chaotic attractors as much as possible, the chaotic attractors can always reflect the chaotic regularity which is difficult to capture in the original time sequence. The chaos theory considers that each component in the system is mutually associated, and the information of the related components is hidden in the development process of any component. This often requires knowledge of the full and sufficient state evolution information in order to obtain complete and accurate qualitative information for the system.
Assuming that a time sequence { x (i) ═ 1,2,3, …, N } is given, the sequence length is N, and based on two important parameters (delay time τ and embedding dimension m), phase space reconstruction is performed on { x (i) }, so that a new phase space matrix is obtained, wherein the new phase space matrix is a matrix formed by high-dimensional vectors:
Figure BDA0003185865820000111
Nmthe phase points of the phase space may be represented as:
X(i)＝[x(i),x(i+τ),x(i+2τ),…,x(i+(m-1)τ)]i＝1,2,…,M
reconstructing a suitable phase space, the key points are the selection of delay time and embedding dimension m:
(1) regarding the delay time τ: if τ is too small, it may happen that the correlation between delay variables is too close, resulting in the time series changing from one-dimensional space to multi-dimensional series of the same variables, and the hidden feature information cannot be found. On the contrary, if the size is too large, the reconstruction will show a severe self-folding phenomenon.
(2) Regarding the embedding dimension m: and if m is too large, the calculation amount is obviously increased, and unnecessary workload is greatly increased and the working efficiency is reduced. If m is too low, the existing defects are highlighted, the attractors can self-cross, the opening is incomplete, and the dynamic behavior of the system cannot be accurately and fully characterized.
The C-C algorithm is adopted in the experiment, and the idea is that the time sequence contains noise and has limited length, the noise and the length are mutually related parameters, and the time sequence and the length are inseparable and need to be determined simultaneously. The algorithm is simple to operate under limited samples, the calculated amount is small, the calculation time is greatly shortened, and the anti-noise capability is strong. The delay time τ being dependent on the time window τwUsing correlation integration to estimate τ and τ simultaneously as (m-1) τwAnd thus determines the embedding dimension m.
The original time sequence is x (i), i is 1,2, …, N, and is divided into t disjoint subsequences, and the length is defined
Comprises the following steps:
l＝[N/t]
wherein [. cndot. ] represents rounding. the t subsequences are spread as follows:
Figure BDA0003185865820000112
the statistics S (m, N, r, τ) for each subsequence are calculated separately:
Figure BDA0003185865820000121
in the formula, ClThe correlation integral representing the ith subsequence can be used for representing that the radius of the neighborhood is larger than that of the phase space
The probability of the distance between any two points is defined as follows:
Figure BDA0003185865820000122
wherein, M is N- (M-1) t, r represents space distance threshold, and theta (x) is Heaviside
Step function, space vector XiAnd XjThe distance between the two is infinite norm.
When N → ∞, there are:
Figure BDA0003185865820000123
according to the BDS statistics, if the time series is independent and homogeneous, S (m, r, τ) is constantly 0 when N → ∞. S (m, r, τ) - τ reflect the autocorrelation of the sequence, which, when it crosses zero for the first time or is the smallest difference for all r,
and the reconstructed chaotic attractor is completely opened in the motion track of the phase space.
Define the maximum deviation Δ S (m, τ) with respect to r:
ΔS(m,τ)＝max{S(m,ri,τ)}-min{S(m,ri,τ)}
where Δ S (m, τ) is used to measure the maximum deviation of S (m, r, τ) from τ to all r.
The optimal delay time is taken as the first zero of S (m, r, τ) to τ or the first minimum of Δ S (m, τ) to τ.
Figure BDA0003185865820000124
Figure BDA0003185865820000125
Figure BDA0003185865820000126
Find out
Figure BDA0003185865820000127
First zero or
Figure BDA0003185865820000128
The first local minimum of (2), the time at which these two points are located, one
These two times are generally used as the optimum delay time τ. While embedding the window width tauwIs from ScorSelecting the time corresponding to the global minimum point in (t) as tauw. Further an optimal embedding dimension m can be determined.
m＝τw/τ+1
In one embodiment, the correlation dimension and the maximum Lyapunov index are calculated for the T-wave-electricity alternation data of the electrocardiogram data by using a chaos detection method, and the method comprises the following steps:
and selecting the first space vector and the second space vector, calculating according to the first space vector and the second space vector to obtain association integrals, and calculating according to the association integrals to obtain the association dimensions.
In one embodiment, after determining whether the first error is smaller than the first threshold and the second error is smaller than the second threshold, the method includes the steps of:
and when the first error is larger than the first threshold value and the second error is larger than the second threshold value, ending the process.
It will be appreciated that the calculation of the correlation dimension: the attractor tracks in the chaotic system phase space form a self-similar structure with infinite nesting, and the phenomenon can be characterized by a correlation dimension. Therefore, the correlation dimension is taken as an important parameter for chaos detection analysis.
After phase space reconstruction is carried out on any time sequence { x (i) }, i ═ 1,2,3, …, N }, any two space vectors X (i) and X (j) are selected, and the proportion of the number of point pairs with the distance between the two space vectors X (i) and X (j) being less than or equal to any given neighborhood radius r to all the point pairs is counted, namely, correlation integral:
Figure BDA0003185865820000131
wherein M is N- (M-1) t, and r represents a spatial distanceThreshold value, theta (X) is Heaviside step function, space vector XiAnd XjThe distance between the two is infinite norm. The correlation integral of sufficiently small r satisfies C (r) · rDThe growth trend is exponential, defining the correlation dimension:
Figure BDA0003185865820000132
d can be estimated from the slope of the linear portion of the curves lnC (r) ln (r):
Figure BDA0003185865820000133
the correlation dimension D is considered when the phase space dimension increases and the value of D does not change with it. The method is simple and easy to implement, is widely applied day by day, is sensitive to noise, and sometimes has larger errors in calculation results.
Figure BDA0003185865820000141
Calculation of the maximum Lyapunov exponent the Lyapunov exponent refers to the average divergence rate exponent of neighboring trajectories in phase space. According to the theory of chaos, the generation of chaos is marked as long as the Lyapunov exponent has a positive value, and the larger the value is, the stronger the chaos degree is. Conversely, if the Lyapunov exponent is negative, it indicates that the system is in an ordered state and the motion is in a stable and convergent state. Therefore, whether the system is chaotic or not can be checked through the positive and negative conditions of the Lyapunov exponent. Obtaining a reconstructed phase space Y (t)i) And (3) after:
Y(ti)＝[x(ti),x(ti+τ),…,x(ti+(m-1)τ)]i＝1,2,…,N
taking an initial point Y (t)0) Find its nearest neighbor point, denoted as Y0(t0). Let it and nearest neighbor point Y0(t0) Is an initial distance L0I.e. the minimum distance between two points. Track bothTime evolution of phase points until t1At the moment of time, the time of day,
the distance between two phase points exceeds a certain value, defined as follows:
L'0＝|Y(t1)-Y0(t1)|>ε
ε>at 0, Y (t) is retained1) And in Y (t)1) Adjacent to another phase point Y1(t1) So that
L1＝|Y(t1)-Y1(t1)|<ε
The above process continues to be repeated until, at a point throughout the sequence, y (t) reaches the end N of the time series.
Assuming that the tracking evolution process iterates M times in total, the maximum Lyapunov exponent (LLE) is:
Figure BDA0003185865820000142
the delay time tau and the embedding dimension m are obtained by computing phase space reconstruction by a C-C algorithm. Some of the results are given in the following table:
Figure BDA0003185865820000151
the data show that the heart system is in a chaotic state, and the pathological state is obviously weaker than the normal chaotic state. And another important information is obtained, namely the minimum of TWA patients is obviously different from the other three types, so that the maximum Lyapunov index and the correlation dimension are selected together to be used as the chaotic characteristic for detecting the TWA.
A sudden cardiac death detection design based on chaotic characteristics has remarkable distinguishing degree between TWA patients and healthy people and other disease patients in two chaotic characteristics (correlation dimension and maximum Lyapunov index).
Defining an error value epsilon1：
Figure BDA0003185865820000152
Wherein the content of the first and second substances,
Figure BDA0003185865820000153
represents the mean of the correlation dimensions, D' represents the sum
Figure BDA0003185865820000154
The correlation dimension value with the largest difference value.
Also, an error value ε is defined2:
Figure BDA0003185865820000155
Wherein the content of the first and second substances,
Figure BDA0003185865820000156
represents the average of the maximum Lyapunov index, and L' represents the sum
Figure BDA0003185865820000157
The maximum Lyapunov exponent with the largest difference.
Calculating the correlation dimension D of the TWA patients in the existing international database (the paper selects 100 groups) and averaging to obtain the correlation dimension D of the TWA patients
Figure BDA0003185865820000158
Is 0.877, and the error value of the correlation dimension is obtained
Figure BDA0003185865820000159
By analogy, the maximum Lyapunov exponent
Figure BDA00031858658200001510
Mean value of 0.0423, error value thereof
Figure BDA00031858658200001511
Will epsilon1、ε2As an index for TWA detection.
Assuming that a piece of electrocardiogram data to be tested { x (i) }, i ═ 1,2,3, …, N } is input, through chaos detection,calculating the correlation dimension and the maximum Lyapunov exponent value to obtain the correlation dimension D of the time series1And the maximum Lyapunov exponent L1Calculating the mean of the correlation dimensions
Figure BDA0003185865820000161
Dimension D associated with the time series to be measured1Absolute value of difference between and maximum Lyapunov exponential mean
Figure BDA0003185865820000162
And the maximum Lyapunov index L of the time sequence to be detected1The absolute value of the difference between. If it satisfies
Figure BDA0003185865820000163
And
Figure BDA0003185865820000164
the electrocardio data to be detected can be judged to belong to TWA data to warn, so that the early warning aim of sudden cardiac death is fulfilled.
Referring to fig. 8, fig. 8 is a structural diagram of a sudden cardiac death warning system for chaotically detecting T-wave electric alternation according to another embodiment of the present invention.
The invention also provides a chaotic detection T-wave electricity alternate sudden cardiac death early warning system, which comprises: the device comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the chaotic detection T-wave electricity alternating sudden cardiac death early warning method when executing the computer program. The chaotically-detected T-wave electricity alternating sudden cardiac death early warning system applies the chaotically-detected T-wave electricity alternating sudden cardiac death early warning method in the first aspect, can search for chaos characteristics with high TWA detection feasibility, effectively detect TWA, and achieve good early warning on sudden cardiac death.
Furthermore, an embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions for execution by one or more control processors, e.g., the control processors are capable of performing steps S110 to S160 of the method of fig. 1.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.

Claims (9)

1. A chaotic detection T-wave electricity alternate sudden cardiac death early warning method is characterized by comprising the following steps:
acquiring electrocardiogram data;
interference noise removal is carried out on the electrocardio data;
calculating T-wave electricity alternation data of the electrocardiogram data by using a chaos detection method to obtain a correlation dimension and a maximum Lyapunov index;
calculating to obtain a first error and a second error, wherein the first error is an absolute value of a difference value between the correlation dimension and an average value of the correlation dimension, and the second error is an absolute value of a difference value between the maximum Lyapunov exponent and an average value of the maximum Lyapunov exponent;
judging whether the first error is smaller than a first threshold value or not, and judging whether the second error is smaller than a second threshold value or not;
and when the first error is smaller than a first threshold value or the second error is smaller than a second threshold value, sending a sudden cardiac death early warning.
2. The chaotic detection T wave electric alternating sudden cardiac death warning method according to claim 1, further comprising the steps of, before the preprocessing of the electrocardiographic data to remove interference noise:
selecting leads and sampling time points of the electrocardiogram data;
and carrying out visual processing on the electrocardiogram data.
3. The chaos detection T wave and electricity alternating sudden cardiac death early warning method according to claim 1, wherein the preprocessing is performed on the electrocardiogram data to remove interference noise, comprising the steps of;
and preprocessing the electrocardio data to remove myoelectric interference and power frequency interference and correct baseline drift.
4. The chaotic detection T-wave and electric alternating sudden cardiac death early warning method according to claim 3, wherein the preprocessing is performed on the electrocardio data to remove myoelectric interference, power frequency interference and baseline drift, and the method comprises the following steps;
filtering the electromyographic interference by using a Butterworth low-pass filter;
filtering the power frequency interference by using a 50Hz or 60Hz power frequency trap;
the baseline wander is corrected using a median filtered Kaiser window function.
5. The sudden cardiac death warning method based on chaotic detection of T-wave electric alternation as claimed in claim 1, wherein before the correlation dimension and the maximum Lyapunov index are calculated by the chaotic detection method for the T-wave electric alternation data of the electrocardiographic data, the method further comprises the following steps:
and selecting a power spectrum, and performing phase space reconstruction on the time sequence.
6. The sudden cardiac death warning method based on chaos detection and T-wave-electricity alternation as claimed in claim 1, wherein a chaos detection method is used for calculating T-wave-electricity alternation data of the electrocardiogram data to obtain a correlation dimension and a maximum Lyapunov index, comprising the steps of:
selecting a first space vector and a second space vector, calculating according to the first space vector and the second space vector to obtain an association integral, and calculating according to the association integral to obtain the association dimension.
7. The chaos detection T-wave and power alternation sudden cardiac death warning method according to claim 1, wherein after determining whether the first error is smaller than a first threshold and determining whether the second error is smaller than a second threshold, comprising the steps of:
and when the first error is larger than a first threshold value and the second error is larger than a second threshold value, ending the process.
8. A chaotic detection T-wave electricity alternate sudden cardiac death early warning system comprises: memory, processor and computer program stored on the memory and executable on the processor, wherein the processor implements the method of chaotically detecting sudden cardiac death in alternation according to any one of claims 1 to 7.
9. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method of chaotically detecting sudden cardiac death in alternation according to any one of claims 1-7.
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