WO2021042589A1 - Extremum energy decomposition-based electrocardiogram signal analysis method - Google Patents

Extremum energy decomposition-based electrocardiogram signal analysis method Download PDF

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WO2021042589A1
WO2021042589A1 PCT/CN2019/121414 CN2019121414W WO2021042589A1 WO 2021042589 A1 WO2021042589 A1 WO 2021042589A1 CN 2019121414 W CN2019121414 W CN 2019121414W WO 2021042589 A1 WO2021042589 A1 WO 2021042589A1
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signal
extreme
energy
modal function
value
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Chinese (zh)
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周作建
宁新宝
曾彭
姜晓东
王�华
刘红星
王斌斌
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江苏华康信息技术有限公司
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/02Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024Detecting, measuring or recording pulse rate or heart rate
    • A61B5/0245Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • A61B5/318Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271Specific aspects of physiological measurement analysis

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  • the invention relates to an electrocardiogram signal analysis, in particular to an electrocardiogram signal analysis method based on an extreme value energy decomposition method.
  • the ECG (electrocardiogram) signal reflects the electrical activity process of the heart beat, and has important guiding significance for the basic functions of the heart and the diagnosis of diseases.
  • the ECG signal includes several different wave groups, such as P wave, QRS wave, and T wave. Each wave group contains different frequency components, and the energy distribution ratios of different frequency components in the ECG signal are different. Studies have shown that 99% of the energy of the ECG signal is concentrated in the range of 0 to 40 Hz. For different wave groups, the frequency distribution range and energy ratio have certain rules.
  • Heart disease can cause changes in the energy distribution of ECG signals. Studying the energy distribution of ECG has important clinical guiding significance for revealing the functional changes caused by heart diseases.
  • ECG signals are non-stationary and nonlinear signals.
  • Traditional frequency domain analysis methods are more suitable for analyzing stationary signals and can only Gives global frequency information.
  • segmenting the frequency domain the traditional method often divides according to several fixed frequency points, without considering the fluctuation characteristics of different signals and the differences between the signals.
  • the object of the present invention is to provide an ECG signal analysis method based on extreme energy decomposition method that can use less data and can intuitively reflect the true law of ECG energy distribution and signal fluctuation.
  • the present invention discloses an ECG signal analysis method based on extreme energy decomposition method, which includes the following steps:
  • the specific preprocessing method is: pass the ECG signal through a 40Hz zero-phase FIR low-pass filter to remove high-frequency noise, and then pass a median filter to remove the baseline drift;
  • n extremum modal function components obtained by decomposing the original signal x(t) represent the components of different frequency bands of the original signal, and then calculate the energy of each component
  • 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;
  • 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;
  • the determination condition of the extreme value modal function in the step (4) is: (a). In the entire data sequence, 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 to the time axis.
  • represents the screening threshold, which is between 0.2 and 0.3.
  • the probability standard value in the steps (8) and (9) is 0.05.
  • the repetition frequency of the waveform of the sixth extremum modal function component obtained in the step (5) is the human heart rate, that is, the cardiac cycle.
  • the present invention uses an extreme energy decomposition method (EED) to analyze the ECG signal, and decomposes the original signal into multiple components, that is, extreme components Function, calculate the energy of each component, obtain its energy distribution, and determine whether the signal is normal or not according to the comparison of the energy values of the components at different levels; the present invention can decompose the signal into subordinates according to the fluctuation law of the biomedical signal itself Signals with different time levels from high frequency to low frequency; and for ECG signals, the repetition frequency can be determined from a special level, that is, the heart rate value of the ECG signal (including the instantaneous heart rate value and the average heart rate value).
  • EED extreme energy decomposition method
  • the method of obtaining the heart rate value is better than The traditional method is more accurate; the data length obtained by extreme value decomposition at all levels is the same, so the data length will not be reduced, so that it can be used for short-term data analysis, that is, it requires a small amount of data to analyze and get accurate results ; EED is not easy to be affected by noise for different levels of component energy analysis.
  • 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.
  • FIG. 6 is a schematic diagram of EED decomposition of ECG signals in Embodiment 1 of the present invention.
  • FIG. 7 is a spectrum diagram of the ECG signal in Embodiment 1 of the present invention.
  • Embodiment 8 is a schematic diagram of EED decomposition of ECG signals in Embodiment 2 of the present invention.
  • FIG. 9 is a time-frequency diagram of ECG of a healthy person in implementation 2 of the present invention.
  • Fig. 10 is a time-frequency diagram of the ECG of CHF patients in the second embodiment of the present invention.
  • an ECG signal analysis method based on the extreme value energy decomposition method of the present invention includes the following steps:
  • the specific preprocessing method is: pass the ECG signal through a 40Hz zero-phase FIR low-pass filter to remove high-frequency noise, and then pass a median filter to remove the baseline drift;
  • 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;
  • n extremum modal function components obtained by decomposing the original signal x(t) represent the components of different frequency bands of the original signal, and then calculate the energy of each component
  • 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;
  • 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;
  • Probability on each extremum mode function component normalization energy distribution vector p i and a significance detector (8) the normalized unknown state ECG signal energy distribution vector p i and the standard ECG signals to obtain Value P i , judge whether the probability value P 1 on the first extreme value modal function component and the probability value P 2 on the second extreme value modal function component are less than the probability standard value, if not, return to step (1) again Obtain the signal; the standard value of probability is selected as 0.05;
  • the repetition frequency of the sixth extreme value modal function component waveform obtained by the present invention is the human heart rate, that is, the cardiac cycle.
  • 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:
  • 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;
  • condition (a) is similar to the requirement of Gaussian normal stationary process for traditional narrowband, condition (b) guarantees 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.
  • An ECG signal analysis method based on the extreme value energy decomposition method of the present invention includes the following steps:
  • the specific preprocessing method 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 pass the median filter to remove baseline drift;
  • the ECG signal can be divided into three wave groups: P, QRS, and T waves; according to the decomposition of 8 extreme value modal function components, c 1 (t) and c 2 (t ) With the highest frequency represents the decomposition component of the QRS complex with the highest frequency in the ECG signal, c 3 (t) represents the decomposition component of the high frequency QRS complex and the P wave in the ECG signal, and c 4 (t) represents the high frequency in the ECG signal.
  • the cardiac cycle, c 7 (t) and c 8 (t) represent the cardiac physiological adjustment rhythm on a larger time scale representing the long-term rhythm of the heart; the amplitude of the observed signal, the highest frequency component has a
  • the center frequency of c 1 is about 20 Hz, and the main frequency is concentrated in the range of 15 to 25 Hz; as the existing research shows, the spectrum range of P wave is 0 to 18 Hz ( ⁇ 3Hz), the energy is mainly concentrated in 5 ⁇ 12Hz; the spectrum range of QRS wave is 0 ⁇ 37Hz ( ⁇ 5Hz), the energy is mainly concentrated in 6 ⁇ 18Hz; the spectrum range of T wave is 0 ⁇ 8Hz ( ⁇ 2Hz), the energy is mainly Focus on 0 ⁇ 8Hz.
  • the frequency band of the QRS complex mainly contains two components, c 1 and c 2
  • the P wave mainly contains two components c 3 and c 4
  • the T wave mainly contains the components c 4 ⁇ c 8 section.
  • each component is only determined by a specific ECG wave group (P, QRS, T wave), or that each ECG wave group is only contained in a specific component.
  • the upper wave group ——Energy relationship is a major correspondence, not all.
  • the c 5 to c 8 components representing the low frequency part are the result of the superposition of the low frequency parts of each ECG wave group, rather than a specific ECG wave group.
  • the extreme value modal function components of the present invention and the extreme value modal function components of each level all show that the extreme value modal function components of ECG can represent a certain ECG wave group fluctuation situation, and reflect the fluctuation law of ECG at different levels; Compared with the traditional frequency domain analysis method, the EED method can directly observe the fluctuation of ECG at various levels, which is very intuitive.
  • the repetition frequency of the waveform of the sixth extremum modal function component obtained is the human heart rate, that is, the cardiac cycle.
  • the EED analysis method is used to analyze the energy distribution of ECG in healthy people and CHF patients at different levels.
  • An ECG signal analysis method based on extreme energy decomposition method for healthy people includes the following steps:
  • the specific preprocessing method 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 pass the median filter to remove baseline drift;
  • 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
  • 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.
  • An ECG signal analysis method based on extreme energy decomposition method for CHF patients includes the following steps:
  • the ECG signal x(t) is subjected to denoising preprocessing;
  • the specific method of 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 pass the median filter to remove baseline drift;
  • 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
  • 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;
  • 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;
  • the energy of healthy people is higher than that of patients with CHF.
  • the energy of healthy people gradually decreases; while patients with CHF gradually increase at levels 1 to 3 and reach the highest value at level 3.
  • the level is greater than 3, the energy gradually decreases as the level increases; the energy of healthy people is mainly concentrated on levels 1 to 4, which is in the high frequency range, indicating that the short-term regulation ability of the heart of healthy people is stronger; the low-level energy of CHF patients The relative decrease indicates that the short-term adjustment ability is reduced, and the main power is concentrated on the middle level.
  • CHF patients are higher than healthy people, indicating that CHF patients have a higher level of regulating heart rhythm. The proportion of energy.
  • the normalized ECG signal CHF patients normalized energy in the energy distribution of the vectors p i and healthy human ECG signal distribution vector p i for a significance test to get on each extremum mode function component the probability value P i, P i when the probability value less than 0.05 indicates a significant difference between the two, as shown in the table, at the level of 1,2,3,5,6 significant difference was found between the two, CHF patients
  • Decrease in energy at low levels of decomposition may indicate that diseases cause a decrease in the heart's ability to regulate on small time scales; while healthy people have higher energy at low levels of decomposition, indicating that healthy people's hearts have better short-term adjustment capabilities and have better short-term adjustment capabilities to the external environment. Have a better ability to adapt to changes in the physical environment;

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Abstract

An extremum energy decomposition-based electrocardiogram signal analysis method, comprising: acquiring an ECG signal x(t) in an unknown state at a given time and at a given sampling frequency; performing de-noising preprocessing on the ECG signal x(t); taking the de-noised ECG signal x(t) as an original signal, decomposing the original signal x(t) into n extremum mode function components and a margin, the n extremum mode function components, obtained by decomposing the original signal x(t), representing components of different frequency bands of the original signal; and determining, according to the n extremum mode function components, whether the ECG signal is an abnormal electrocardiogram signal. The ECG signal is analyzed by means of extremum energy decomposition, the original signal is decomposed into a plurality of components, i.e. extremum component functions, and the energy of each component is calculated to obtain the energy distribution thereof.

Description

一种基于极值能量分解法的心电图信号分析方法An ECG signal analysis method based on extreme energy decomposition method 技术领域Technical field
本发明涉及一种心电图信号分析,尤其涉及一种基于极值能量分解法的心电图信号分析方法。The invention relates to an electrocardiogram signal analysis, in particular to an electrocardiogram signal analysis method based on an extreme value energy decomposition method.
背景技术Background technique
生理信号是由生命体多个系统相互作用产生的,不同系统作用的时间和强度不同,导致生理信号具有时间和空间上的复杂性。ECG(electrocardiogram)信号反应了心脏搏动的电活动过程,对于心脏基本功能和疾病诊断有重要的指导意义。ECG信号包括P波、QRS波、T波等几个不同的波群,每一个波群包含不同的频率成分,不同的频率成分在ECG信号中的能量分布比例不同。研究表明,ECG信号99%的能量集中在0~40Hz的范围内,对于不同的波群,频率分布范围和能量比例都有一定的规律。心脏疾病会引起ECG信号能量分布发生改变,研究ECG能量分布,对于揭示心脏疾病引起的功能改变有重要的临床指导意义。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. The ECG (electrocardiogram) signal reflects the electrical activity process of the heart beat, and has important guiding significance for the basic functions of the heart and the diagnosis of diseases. The ECG signal includes several different wave groups, such as P wave, QRS wave, and T wave. Each wave group contains different frequency components, and the energy distribution ratios of different frequency components in the ECG signal are different. Studies have shown that 99% of the energy of the ECG signal is concentrated in the range of 0 to 40 Hz. For different wave groups, the frequency distribution range and energy ratio have certain rules. Heart disease can cause changes in the energy distribution of ECG signals. Studying the energy distribution of ECG has important clinical guiding significance for revealing the functional changes caused by heart diseases.
在ECG的能量(频谱)研究方面,传统经典方法,比如频谱分析、时域分析等已有很多,然而,ECG信号是非平稳非线性信号,传统频域分析方法更适合分析平稳信号,并且只能给出全局频率信息。另外对频域进行分段时,传统方法往往根据几个固定的频率点进行划分,没有考虑不同信号自身的波动特点和各信号之间的差异。In the energy (spectrum) research of ECG, there are many traditional classical methods, such as spectrum analysis, time domain analysis, etc. However, ECG signals are non-stationary and nonlinear signals. Traditional frequency domain analysis methods are more suitable for analyzing stationary signals and can only Gives global frequency information. In addition, when segmenting the frequency domain, the traditional method often divides according to several fixed frequency points, without considering the fluctuation characteristics of different signals and the differences between the signals.
因此,亟待解决上述问题。Therefore, it is urgent to solve the above-mentioned problems.
发明内容Summary of the invention
发明目的:本发明的目的是提供一种可采用较少数据即可直观反映心电图能量分布和信号波动的真实规律的基于极值能量分解法的心电图信号分析方法。Object of the invention: The object of the present invention is to provide an ECG signal analysis method based on extreme energy decomposition method that can use less data and can intuitively reflect the true law of ECG energy distribution and signal fluctuation.
技术方案:为实现以上目的,本发明公开了一种基于极值能量分解法的心电图信号分析方法,包括如下步骤:Technical solution: In order to achieve the above objectives, the present invention discloses an ECG signal analysis method based on extreme energy decomposition method, which includes the following steps:
(1)、获取给定时间和给定采样频率下的未知状态的ECG信号x(t);(1) Obtain the ECG signal x(t) in an unknown state at a given time and a given sampling frequency;
(2)、将ECG信号x(t)进行去噪预处理;预处理的具体方法为:将ECG信号经过40Hz零相位FIR低通滤波器滤波消除高频噪声,然后经过中值滤波器去除基线漂移;(2) Perform denoising preprocessing of the ECG signal x(t); the specific preprocessing method is: pass the ECG signal through a 40Hz zero-phase FIR low-pass filter to remove high-frequency noise, and then pass a median filter to remove the baseline drift;
(3)、将去噪后的ECG信号x(t)作为原始信号,求出原始信号的所有局部极值点,然后将原始信号的所有极大值点和所有极小值点采用样条曲线连起来分别形成上包络 线e max和下包络线e min,得到上、下包络线的包络均值信号m(t)=(e max+e min)/2; (3) Take the denoised ECG signal x(t) as the original signal, find all the local extreme points of the original signal, and then use the spline curve for all the maximum points and all the minimum points of the original signal They are connected to form the upper envelope e max and the lower envelope e min respectively , and the envelope mean signal m(t)=(e max +e min )/2 of the upper and lower envelopes is obtained;
(4)、将原始信号x(t)减去包络均值信号m(t),得到h(t)=x(t)-m(t);然后判断h(t)是否满足极值模态函数的判定条件,如果不满足,将h(t)作为原始信号返回至步骤(3),直到h k(t)满足极值模态函数的判定条件,则记c 1(t)=h k(t),作为第一个极值模态函数分量; (4) 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;
(5)、将原始信号x(t)减去第一个极值模态函数分量c 1(t),得到余量r 1(t)=x(t)-c 1(t),然后判断h k(t)是否满足停止准则,如果不满足,将r 1(t)作为新的原始序列x(t),返回至步骤(3)和(4),直到h k(t)满足停止准则,得到第2、3、…、n个极值模态函数分量及余量r n(t),于是将原始信号x(t)分解为n个极值模态函数分量和一个余量,即 (5) 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 (3) and (4) until h k (t) meets the stopping criterion , Get the 2, 3,..., n extremum modal function components and margin r n (t), then decompose the original signal x(t) into n extremum modal function components and a margin, namely
Figure PCTCN2019121414-appb-000001
Figure PCTCN2019121414-appb-000001
(6)、对极值模态函数分量c i(t),i=1,2,…,n,进行频谱分析得到各极值模态函数分量的中心频率; (6) 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;
(7)、将原始信号x(t)分解得的n个极值模态函数分量,代表了原始信号不同频段的分量,然后计算其各个分量的能量(7) The n extremum modal function components obtained by decomposing the original signal x(t) represent the components of different frequency bands of the original signal, and then calculate the energy of each component
E i=∫|c i(t)| 2dt,i=1,2,…,n E i =∫|c i (t)| 2 dt,i=1,2,...,n
将每一个能量值归一化,得到归一化的能量分布向量Normalize each energy value to get a normalized energy distribution vector
p i=E i/E,i=1,2,…,n p i =E i /E,i=1,2,...,n
其中,
Figure PCTCN2019121414-appb-000002
第一个分量p 1表示最高频段的能量,代表了信号在最高频段范围内能量分布的比例,最后一个分量p n表示信号在最低频段范围内能量分布的比例;根据归一化的能量分布向量绘制归一化能量分布图,其中横坐标表示分量层次,纵坐标表示归一化的能量分布向量值,曲线表示平均值,误差棒表示标准差;
among them,
Figure PCTCN2019121414-appb-000002
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;
(8)、将未知状态的ECG信号的归一化的能量分布向量p i和标准ECG信号的归一化的能量分布向量p i进行显著性检测得到每一个极值模态函数分量上的概率值P i,判断第一极值模态函数分量上的概率值P 1和第二极值模态函数分量上的概率值P 2是否小于概率标准值,若不小于则返回步骤(1)重新获取信号; Probability on each extremum mode function component normalization energy distribution vector p i and a significance detector (8), the normalized unknown state ECG signal energy distribution vector p i and the standard ECG signals to obtain Value P i , judge whether the probability value P 1 on the first extreme value modal function component and the probability value P 2 on the second extreme value modal function component are less than the probability standard value, if not, return to step (1) again Get the signal;
(9)、在满足P 1和P 2小于概率标准值的条件下,若第一个极值模态函数分量的能量分布向量p 1小于第二个极值模态函数分量的能量分布向量p 2,则判定该ECG信号为异常心电图信号。 (9) Under the condition that P 1 and P 2 are less than the probability standard value, if the energy distribution vector p 1 of the first extreme value modal function component is smaller than the energy distribution vector p of the second extreme value modal function component 2 , it is determined that the ECG signal is an abnormal electrocardiogram signal.
其中,所述原始信号x(t)所需最少数据量N=2 n+1,其中n为分解出的极值模态函数分量的数量。 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.
优选的,所述步骤(4)中极值模态函数的判定条件为:(a)、在整个数据序列中,极值点的数量与过零点的数量相等或者相差一个;(b)、在任意时刻,上下包络线对于时间轴对称。Preferably, the determination condition of the extreme value modal function in the step (4) is: (a). In the entire data sequence, 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 to the time axis.
再者,所述步骤(5)中h k(t)满足停止准则的公式为: Furthermore, the formula for h k (t) satisfying the stopping criterion in the step (5) is:
Figure PCTCN2019121414-appb-000003
ε表示筛选门限,取0.2~0.3之间。
Figure PCTCN2019121414-appb-000003
ε represents the screening threshold, which is between 0.2 and 0.3.
进一步,所述步骤(8)和步骤(9)中的概率标准值为0.05。Further, the probability standard value in the steps (8) and (9) is 0.05.
再者,所述步骤(5)中得到的第6个极值模态函数分量波形的重复频率为人体心率,即心动周期。Furthermore, the repetition frequency of the waveform of the sixth extremum modal function component obtained in the step (5) is the human heart rate, that is, the cardiac cycle.
有益效果:与现有技术相比,本发明具有以下显著优点:本发明采用极值能量分解方法(Extremum Energy Decomposition,EED)分析ECG信号,将原始信号分解为多个分量,也就是极值分量函数,计算每一个分量的能量,得到其能量分布,且可依据不同层次分量能量值的比较可判定其信号的正常与否;本发明的可依据生物医学信号自身的波动规律将信号分解为从高频到低频的不同时间层次信号;且对于ECG信号,从其一个特殊层次可确定其重复频率,即ECG信号的心率值(包括瞬时心率值和平均心率值),该获得心率值的方法比传统方法较为精确;极值分解在所有层次上得到的数据长度相同,因而不会导致数据长度减小,从而使其可以用于短时间数据分析,即需要很少数据量即可分析得到准确结果;EED对于不同层次分量能量分析不容易受噪声的影响。Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: the present invention uses an extreme energy decomposition method (EED) to analyze the ECG signal, and decomposes the original signal into multiple components, that is, extreme components Function, calculate the energy of each component, obtain its energy distribution, and determine whether the signal is normal or not according to the comparison of the energy values of the components at different levels; the present invention can decompose the signal into subordinates according to the fluctuation law of the biomedical signal itself Signals with different time levels from high frequency to low frequency; and for ECG signals, the repetition frequency can be determined from a special level, that is, the heart rate value of the ECG signal (including the instantaneous heart rate value and the average heart rate value). The method of obtaining the heart rate value is better than The traditional method is more accurate; the data length obtained by extreme value decomposition at all levels is the same, so the data length will not be reduced, so that it can be used for short-term data analysis, that is, it requires a small amount of data to analyze and get accurate results ; EED is not easy to be affected by noise for different levels of component energy analysis.
附图说明Description of the drawings
图1为本发明中原始信号的示意图;Figure 1 is a schematic diagram of the original signal in the present invention;
图2为本发明中原始信号求取包络线的示意图;Fig. 2 is a schematic diagram of obtaining an envelope of an original signal in the present invention;
图3为本发明中原始信号的减去包络均值信号的示意图;Fig. 3 is a schematic diagram of the original signal minus the envelope mean value signal in the present invention;
图4为本发明中得到第一个极值模态函数分量的示意图;Fig. 4 is a schematic diagram of the first extremum modal function component obtained in the present invention;
图5为本发明中极值能量分解法的流程示意图;Figure 5 is a schematic flow chart of the extreme value energy decomposition method in the present invention;
图6为本发明实施例1中ECG信号的EED分解示意图;6 is a schematic diagram of EED decomposition of ECG signals in Embodiment 1 of the present invention;
图7为本发明实施例1中ECG信号的频谱图;FIG. 7 is a spectrum diagram of the ECG signal in Embodiment 1 of the present invention;
图8为本发明实施例2中ECG信号的EED分解示意图;8 is a schematic diagram of EED decomposition of ECG signals in Embodiment 2 of the present invention;
图9为本发明实施2中健康人的ECG的时频图;FIG. 9 is a time-frequency diagram of ECG of a healthy person in implementation 2 of the present invention;
图10为本发明实施2中CHF患者的ECG的时频图。Fig. 10 is a time-frequency diagram of the ECG of CHF patients in the second embodiment of the present invention.
具体实施方式detailed description
下面结合附图对本发明的技术方案作进一步说明。The technical scheme of the present invention will be further described below in conjunction with the accompanying drawings.
如图1、图2、图3、图4和图5所示,本发明的一种基于极值能量分解法的心电图信号分析方法,包括如下步骤:As shown in Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5, an ECG signal analysis method based on the extreme value energy decomposition method of the present invention includes the following steps:
(1)、获取给定时间和给定采样频率下的未知状态的ECG信号x(t);原始信号x(t)所需最少数据量N=2 n+1,其中n为分解出的极值模态函数分量的数量; (1) Get the ECG signal x(t) of unknown state at a given time and a given sampling frequency; the minimum amount of data required for the original signal x(t) is N=2 n+1 , where n is the decomposed pole The number of value modal function components;
(2)、将ECG信号x(t)进行去噪预处理;预处理的具体方法为:将ECG信号经过40Hz零相位FIR低通滤波器滤波消除高频噪声,然后经过中值滤波器去除基线漂移;(2) Perform denoising preprocessing of the ECG signal x(t); the specific preprocessing method is: pass the ECG signal through a 40Hz zero-phase FIR low-pass filter to remove high-frequency noise, and then pass a median filter to remove the baseline drift;
(3)、将去噪后的ECG信号x(t)作为原始信号,求出原始信号的所有局部极值点,然后将原始信号的所有极大值点和所有极小值点采用样条曲线连起来分别形成上包络线e max和下包络线e min,得到上、下包络线的包络均值信号m(t)=(e max+e min)/2; (3) Take the denoised ECG signal x(t) as the original signal, find all the local extreme points of the original signal, and then use the spline curve for all the maximum points and all the minimum points of the original signal They are connected to form the upper envelope e max and the lower envelope e min respectively , and the envelope mean signal m(t)=(e max +e min )/2 of the upper and lower envelopes is obtained;
(4)、将原始信号x(t)减去包络均值信号m(t),得到h(t)=x(t)-m(t);然后判断h(t)是否满足极值模态函数的判定条件,如果不满足,将h(t)作为原始信号返回至步骤(3),直到h k(t)满足极值模态函数的判定条件,则记c 1(t)=h k(t),作为第一个极值模态函数分量;其中极值模态函数的判定条件为:(a)、在整个数据序列中,极值点的数量与过零点的数量相等或者相差一个;(b)、在任意时刻,上下包络线对于时间轴对称; (4) 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; 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;
(5)、将原始信号x(t)减去第一个极值模态函数分量c 1(t),得到余量r 1(t)=x(t)-c 1(t),然后判断h k(t)是否满足停止准则,如果不满足,将r 1(t)作为新的原始序列x(t),返回至步骤(3)和(4),直到h k(t)满足停止准则,得到第2、3、…、n个极值模态函数分量及余量r n(t),于是将原始信号x(t)分解为n个极值模态函数分量和一个余量,即 (5) 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 (3) and (4) until h k (t) meets the stopping criterion , Get the 2, 3,..., n extremum modal function components and margin r n (t), then decompose the original signal x(t) into n extremum modal function components and a margin, namely
Figure PCTCN2019121414-appb-000004
Figure PCTCN2019121414-appb-000004
其中h k(t)满足停止准则的公式为: The formula for h k (t) to satisfy the stopping criterion is:
Figure PCTCN2019121414-appb-000005
ε表示筛选门限,取0.2~0.3之间;满足停止准则的极值模态分解则满足如下两个条件:(a)最后得到的极值模态函数分量c n(t)或者余量r n(t)小于预先设定的阈值;(b)残余信号r n(t)成为单调信号,不能从中再提取出极值模态函 数信号;
Figure PCTCN2019121414-appb-000005
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;
(6)、对极值模态函数分量c i(t),i=1,2,…,n,进行频谱分析得到各极值模态函数分量的中心频率; (6) 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;
(7)、将原始信号x(t)分解得的n个极值模态函数分量,代表了原始信号不同频段的分量,然后计算其各个分量的能量(7) The n extremum modal function components obtained by decomposing the original signal x(t) represent the components of different frequency bands of the original signal, and then calculate the energy of each component
E i=∫|c i(t)| 2dt,i=1,2,…,n E i =∫|c i (t)| 2 dt,i=1,2,...,n
将每一个能量值归一化,得到归一化的能量分布向量Normalize each energy value to get a normalized energy distribution vector
p i=E i/E,i=1,2,…,n p i =E i /E,i=1,2,...,n
其中,
Figure PCTCN2019121414-appb-000006
第一个分量p 1表示最高频段的能量,代表了信号在最高频段范围内能量分布的比例,最后一个分量p n表示信号在最低频段范围内能量分布的比例;根据归一化的能量分布向量绘制归一化能量分布图,其中横坐标表示分量层次,纵坐标表示归一化的能量分布向量值,曲线表示平均值,误差棒表示标准差;
among them,
Figure PCTCN2019121414-appb-000006
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;
(8)、将未知状态的ECG信号的归一化的能量分布向量p i和标准ECG信号的归一化的能量分布向量p i进行显著性检测得到每一个极值模态函数分量上的概率值P i,判断第一极值模态函数分量上的概率值P 1和第二极值模态函数分量上的概率值P 2是否小于概率标准值,若不小于则返回步骤(1)重新获取信号;其中概率标准值选为0.05; Probability on each extremum mode function component normalization energy distribution vector p i and a significance detector (8), the normalized unknown state ECG signal energy distribution vector p i and the standard ECG signals to obtain Value P i , judge whether the probability value P 1 on the first extreme value modal function component and the probability value P 2 on the second extreme value modal function component are less than the probability standard value, if not, return to step (1) again Obtain the signal; the standard value of probability is selected as 0.05;
(9)、在满足P 1和P 2小于概率标准值的条件下,若第一个极值模态函数分量的能量分布向量p 1小于第二个极值模态函数分量的能量分布向量p 2,则判定该ECG信号为异常心电图信号。 (9) Under the condition that P 1 and P 2 are less than the probability standard value, if the energy distribution vector p 1 of the first extreme value modal function component is smaller than the energy distribution vector p of the second extreme value modal function component 2 , it is determined that the ECG signal is an abnormal electrocardiogram signal.
本发明得到的第6个极值模态函数分量波形的重复频率为人体心率,即心动周期。The repetition frequency of the sixth extreme value modal function component waveform obtained by the present invention is the human heart rate, that is, the cardiac cycle.
本发明采用的极值能量分解方法(Extremum Energy Decomposition,EED),其是基于极值模态函数的概念的方法,极值模态函数是同时满足下面两个条件的具有单一频率的一类信号,两个条件为: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)、在整个数据序列中,极值点(包括极大值和极小值)的数量与过零点的数量必须相等或者最多相差一个;(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)在任意时刻,局部极大值点形成的上包络线与局部极小值点形成的下包络线的均值为零,也就是说局部上下包络线对于时间轴局部对称;(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;
上面两个条件,条件(a)类似于高斯正态平稳过程对于传统窄带的要求,条件(b) 保证了由极值模态函数计算得到的瞬时频率有物理意义。The above two conditions, condition (a) is similar to the requirement of Gaussian normal stationary process for traditional narrowband, condition (b) guarantees that the instantaneous frequency calculated by the extreme value modal function has physical meaning.
本发明的极值模态函数分解终止的标准选择要适中,条件太严格,会导致最后几个极值模态函数分量失去意义;条件太宽松,会导致有用的分量丢失;在实际应用中,也可以根据需求设定需要分解的极值模态函数分量层数,当满足分解层数时即终止计算。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.
实施例1Example 1
本发明的一种基于极值能量分解法的心电图信号分析方法,包括如下步骤:An ECG signal analysis method based on the extreme value energy decomposition method of the present invention includes the following steps:
(1)、从physionet的健康人正常窦性数据库nsrdb内获取给定时间和给定采样频率下的ECG信号x(t);数据长度为8s,原始信号x(t)所需最少数据量为N=2 n+1=2 9,其中n为分解出的极值模态函数分量的数量,n=8; (1) Obtain the ECG signal x(t) at a given time and a given sampling frequency from the normal sinus database nsrdb of physionet of healthy people; the data length is 8s, and 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 components of the extremum modal function decomposed, n=8;
(2)、将ECG信号x(t)进行去噪预处理;预处理的具体方法为:由于ECG能量主要集中在0~40Hz,将ECG信号经过40Hz零相位FIR低通滤波器滤波消除高频噪声,然后经过中值滤波器去除基线漂移;(2) Perform denoising preprocessing of the ECG signal x(t); the specific preprocessing method 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 pass the median filter to remove baseline drift;
(3)、将去噪后的ECG信号x(t)作为原始信号,求出原始信号的所有局部极值点,然后将原始信号的所有极大值点和所有极小值点采用样条曲线连起来分别形成上包络线e max和下包络线e min,得到上、下包络线的包络均值信号m(t)=(e max+e min)/2; (3) Take the denoised ECG signal x(t) as the original signal, find all the local extreme points of the original signal, and then use the spline curve for all the maximum points and all the minimum points of the original signal They are connected to form the upper envelope e max and the lower envelope e min respectively , and the envelope mean signal m(t)=(e max +e min )/2 of the upper and lower envelopes is obtained;
(4)、将原始信号x(t)减去包络均值信号m(t),得到h(t)=x(t)-m(t);然后判断h(t)是否满足极值模态函数的判定条件,如果不满足,将h(t)作为原始信号返回至步骤(3),直到h k(t)满足极值模态函数的判定条件,则记c 1(t)=h k(t),作为第一个极值模态函数分量;其中极值模态函数的判定条件为:(a)、在整个数据序列中,极值点的数量与过零点的数量相等或者相差一个;(b)、在任意时刻,上下包络线对于时间轴对称; (4) 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; 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;
(5)、将原始信号x(t)减去第一个极值模态函数分量c 1(t),得到余量r 1(t)=x(t)-c 1(t),将r 1(t)作为新的原始序列x(t),返回至步骤(3)和(4),得到第2、3、…、8个极值模态函数分量及余量r 8(t),于是将原始信号x(t)分解为8个极值模态函数分量和一个余量,即 (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 (3) and (4) to obtain the second, third, ..., eighth extreme value 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
Figure PCTCN2019121414-appb-000007
Figure PCTCN2019121414-appb-000007
如图6所示,根据波形特点,ECG信号可以分为P、QRS、T波三个波群;根据分解8个极值模态函数分量,可得出c 1(t)和c 2(t)具有最高的频率表示ECG信号中频率最高的QRS波群的分解成分,c 3(t)表示ECG信号中高频QRS波群和P波叠加的分解成分, c 4(t)表示ECG信号中高频QRS波群、P波和T波叠加的分解成分,c 5(t)表示ECG信号中QRS波群、P波和T波的低频部分叠加的分解成分,c 6(t)表示代表心脏跳动节律的心动周期,c 7(t)和c 8(t)表示代表了心脏的长时节律的更大时间尺度上的心脏生理调整节律;观察信号的幅值,频率最高的分量具有较高的幅值,能量最高;频率最低的分量幅值较低,能量较低; As shown in Figure 6, according to the characteristics of the waveform, the ECG signal can be divided into three wave groups: P, QRS, and T waves; according to the decomposition of 8 extreme value modal function components, c 1 (t) and c 2 (t ) With the highest frequency represents the decomposition component of the QRS complex with the highest frequency in the ECG signal, c 3 (t) represents the decomposition component of the high frequency QRS complex and the P wave in the ECG signal, and c 4 (t) represents the high frequency in the ECG signal The decomposed components of the superposition of QRS complex, P wave and T wave, c 5 (t) represents the decomposed component of the low-frequency part of QRS complex, P wave and T wave in the ECG signal, and c 6 (t) represents the heartbeat rhythm The cardiac cycle, c 7 (t) and c 8 (t) represent the cardiac physiological adjustment rhythm on a larger time scale representing the long-term rhythm of the heart; the amplitude of the observed signal, the highest frequency component has a higher amplitude Value, the energy is the highest; the component with the lowest frequency has the lower amplitude and lower energy;
(6)、对极值模态函数分量c i(t),i=1,2,…,8,进行频谱分析得到各极值模态函数分量的中心频率,如表1;其中对c 1进行频谱分析,得到图7所示频谱图,可得出c 1的中心频率在20Hz左右,主要频率集中在15~25Hz的范围;如现有研究表明,P波的频谱范围为0~18Hz(±3Hz),能量主要集中在5~12Hz;QRS波的频谱范围为0~37Hz(±5Hz),能量主要集中在6~18Hz;T波的频谱范围为0~8Hz(±2Hz),能量主要集中在0~8Hz。对比表1可以看出,QRS波群的频带主要包含了c 1、c 2两个分量,P波主要包含了c 3,c 4两个分量,T波主要包含了c 4~c 8的分量部分。需要注意的是,这里说的包含并不是说每一个分量只由特定的ECG波群(P、QRS、T波)决定,或者每一个ECG波群只包含于特定的分量中,上面的波群——能量关系是一个主要的对应关系,而不是全部。例如,代表低频部分的c 5~c 8分量,是由各个ECG波群的低频部分叠加的结果,而不是由某一特定的ECG波群决定。本发明的极值模态函数分量和各层次的极值模态函数分量均说明ECG的极值模态函数分量能够代表一定的ECG波群波动情况,反应ECG在不同层次上的波动规律;相比较传统的频域分析方法,EED方法可以直接观察ECG在各个层次上的波动情况,非常直观。其中得到的第6个极值模态函数分量波形的重复频率为人体心率,即心动周期。 (6) For the extreme value modal function components c i (t), i = 1, 2, ..., 8, perform spectrum analysis to obtain the center frequency of each extreme value modal function component, as shown in Table 1; where c 1 Perform spectrum analysis to obtain the spectrum diagram shown in Figure 7. It can be concluded that the center frequency of c 1 is about 20 Hz, and the main frequency is concentrated in the range of 15 to 25 Hz; as the existing research shows, the spectrum range of P wave is 0 to 18 Hz ( ±3Hz), the energy is mainly concentrated in 5~12Hz; the spectrum range of QRS wave is 0~37Hz (±5Hz), the energy is mainly concentrated in 6~18Hz; the spectrum range of T wave is 0~8Hz (±2Hz), the energy is mainly Focus on 0~8Hz. Comparing Table 1, it can be seen that the frequency band of the QRS complex mainly contains two components, c 1 and c 2 , the P wave mainly contains two components c 3 and c 4 , and the T wave mainly contains the components c 4 ~c 8 section. It should be noted that the inclusion here does not mean that each component is only determined by a specific ECG wave group (P, QRS, T wave), or that each ECG wave group is only contained in a specific component. The upper wave group ——Energy relationship is a major correspondence, not all. For example, the c 5 to c 8 components representing the low frequency part are the result of the superposition of the low frequency parts of each ECG wave group, rather than a specific ECG wave group. The extreme value modal function components of the present invention and the extreme value modal function components of each level all show that the extreme value modal function components of ECG can represent a certain ECG wave group fluctuation situation, and reflect the fluctuation law of ECG at different levels; Compared with the traditional frequency domain analysis method, the EED method can directly observe the fluctuation of ECG at various levels, which is very intuitive. The repetition frequency of the waveform of the sixth extremum modal function component obtained is the human heart rate, that is, the cardiac cycle.
表1 各个极值模态函数分量的中心频率Table 1 The center frequency of each extreme value modal function component
Figure PCTCN2019121414-appb-000008
Figure PCTCN2019121414-appb-000008
实施例2Example 2
将EED分析方法用于分析健康人和CHF患者ECG在不同层次下的能量分布。The EED analysis method is used to analyze the energy distribution of ECG in healthy people and CHF patients at different levels.
健康人的一种基于极值能量分解法的心电图信号分析方法,包括如下步骤:An ECG signal analysis method based on extreme energy decomposition method for healthy people includes the following steps:
(1)、从physionet的正常窦性数据库nsrdb获取数据长度为10s,采样频率为128Hz的ECG信号x(t);其中nsrdb数据库包含18个健康人(年龄34.3±8.4),原始信号x(t)所需最少数据量N=2 n+1=2 9,其中n为分解出的极值模态函数分量的数量,n=8; (1). Obtain the ECG signal x(t) with a data length of 10s and a sampling frequency of 128Hz from the normal sinus database nsrdb of physionet; the nsrdb database contains 18 healthy people (age 34.3±8.4), the original signal x(t ) The minimum amount of data required is N=2 n+1 =2 9 , where n is the number of decomposed extreme value modal function components, n=8;
(2)、将ECG信号x(t)进行去噪预处理;预处理的具体方法为:由于ECG能量主要集中在0~40Hz,将ECG信号经过40Hz零相位FIR低通滤波器滤波消除高频噪声,然后经过中值滤波器去除基线漂移;(2) Perform denoising preprocessing of the ECG signal x(t); the specific preprocessing method 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 pass the median filter to remove baseline drift;
(3)、将去噪后的ECG信号x(t)作为原始信号,求出原始信号的所有局部极值点,然后将原始信号的所有极大值点和所有极小值点采用样条曲线连起来分别形成上包络线e max和下包络线e min,得到上、下包络线的包络均值信号m(t)=(e max+e min)/2; (3) Take the denoised ECG signal x(t) as the original signal, find all the local extreme points of the original signal, and then use the spline curve for all the maximum points and all the minimum points of the original signal They are connected to form the upper envelope e max and the lower envelope e min respectively , and the envelope mean signal m(t)=(e max +e min )/2 of the upper and lower envelopes is obtained;
(4)、将原始信号x(t)减去包络均值信号m(t),得到h(t)=x(t)-m(t);然后判断h(t)是否满足极值模态函数的判定条件,如果不满足,将h(t)作为原始信号返回至步骤(3),直到h k(t)满足极值模态函数的判定条件,则记c 1(t)=h k(t),作为第一个极值模态函数分量;其中极值模态函数的判定条件为:(a)、在整个数据序列中,极值点的数量与过零点的数量相等或者相差一个;(b)、在任意时刻,上下包络线对于时间轴对称; (4) 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; 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;
(5)、将原始信号x(t)减去第一个极值模态函数分量c 1(t),得到余量r 1(t)=x(t)-c 1(t),将r 1(t)作为新的原始序列x(t),返回至步骤(3)和(4),得到第2、3、…、8个极值模态函数分量及余量r 8(t),于是将原始信号x(t)分解为8个极值模态函数分量和一个余量,即 (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 (3) and (4) to obtain the second, third, ..., eighth extreme value 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
Figure PCTCN2019121414-appb-000009
Figure PCTCN2019121414-appb-000009
(6)、对极值模态函数分量c i(t),i=1,2,…,8,进行频谱分析得到各极值模态函数分量的中心频率,得到频域分析结果图; (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)、将原始信号x(t)分解得的8个极值模态函数分量,代表了原始信号不同频段的分量,然后计算其各个分量的能量(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)| 2dt,i=1,2,…,8 E i =∫|c i (t)| 2 dt,i=1,2,…,8
将每一个能量值归一化,得到归一化的能量分布向量Normalize each energy value to get a normalized energy distribution vector
p i=E i/E,i=1,2,…,8 p i =E i /E,i=1,2,…,8
其中,
Figure PCTCN2019121414-appb-000010
第一个分量p 1表示最高频段的能量,代表了信号在最高频段范围内能量分布的比例,最后一个分量p n表示信号在最低频段范围内能量分布的比例。
among them,
Figure PCTCN2019121414-appb-000010
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.
CHF患者的一种基于极值能量分解法的心电图信号分析方法,包括如下步骤:An ECG signal analysis method based on extreme energy decomposition method for CHF patients includes the following steps:
(1)、从CHF数据库chfdb获取数据长度为10s,采样频率为128Hz的ECG信号 x(t),其中chfdb数据库包含15个CHF患者(年龄58.8±9.1),原始信号x(t)所需最少数据量N=2 n+1=2 9,其中n为分解出的极值模态函数分量的数量,n=8; (1) Get the ECG signal x(t) with a data length of 10s and a sampling frequency of 128Hz from the CHF database chfdb. The chfdb database contains 15 CHF patients (age 58.8±9.1), and the original signal x(t) requires the least The amount of data N=2 n+1 =2 9 , where n is the number of decomposed extreme value modal function components, n=8;
((2)、将ECG信号x(t)进行去噪预处理;预处理的具体方法为:由于ECG能量主要集中在0~40Hz,将ECG信号经过40Hz零相位FIR低通滤波器滤波消除高频噪声,然后经过中值滤波器去除基线漂移;((2), the ECG signal x(t) is subjected to denoising preprocessing; the specific method of 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 pass the median filter to remove baseline drift;
(3)、将去噪后的ECG信号x(t)作为原始信号,求出原始信号的所有局部极值点,然后将原始信号的所有极大值点和所有极小值点采用样条曲线连起来分别形成上包络线e max和下包络线e min,得到上、下包络线的包络均值信号m(t)=(e max+e min)/2; (3) Take the denoised ECG signal x(t) as the original signal, find all the local extreme points of the original signal, and then use the spline curve for all the maximum points and all the minimum points of the original signal They are connected to form the upper envelope e max and the lower envelope e min respectively , and the envelope mean signal m(t)=(e max +e min )/2 of the upper and lower envelopes is obtained;
(4)、将原始信号x(t)减去包络均值信号m(t),得到h(t)=x(t)-m(t);然后判断h(t)是否满足极值模态函数的判定条件,如果不满足,将h(t)作为原始信号返回至步骤(3),直到h k(t)满足极值模态函数的判定条件,则记c 1(t)=h k(t),作为第一个极值模态函数分量;其中极值模态函数的判定条件为:(a)、在整个数据序列中,极值点的数量与过零点的数量相等或者相差一个;(b)、在任意时刻,上下包络线对于时间轴对称; (4) 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; 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;
(5)、将原始信号x(t)减去第一个极值模态函数分量c 1(t),得到余量r 1(t)=x(t)-c 1(t),将r 1(t)作为新的原始序列x(t),返回至步骤(3)和(4),得到第2、3、…、8个极值模态函数分量及余量r 8(t),于是将原始信号x(t)分解为8个极值模态函数分量和一个余量,即 (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 (3) and (4) to obtain the second, third, ..., eighth extreme value 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
Figure PCTCN2019121414-appb-000011
Figure PCTCN2019121414-appb-000011
(6)、对极值模态函数分量c i(t),i=1,2,…,8,进行频谱分析得到各极值模态函数分量的中心频率,得到频域分析结果图,如图9所示,健康人能量分布在0~40Hz的范围内,并且在20Hz以上的高频部分能量比例较高,如图10所示,CHF能量主要集中在20Hz以下,高频部分能量明显减少; (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 graph, such as As shown in Figure 9, the energy distribution of healthy people is in the range of 0-40Hz, and the high-frequency energy above 20Hz has a higher proportion. As shown in Figure 10, the CHF energy is mainly concentrated below 20Hz, and the high-frequency energy is significantly reduced ;
(7)、将原始信号x(t)分解得的8个极值模态函数分量,代表了原始信号不同频段的分量,然后计算其各个分量的能量(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)| 2dt,i=1,2,…,8 E i =∫|c i (t)| 2 dt,i=1,2,…,8
将每一个能量值归一化,得到归一化的能量分布向量Normalize each energy value to get a normalized energy distribution vector
p i=E i/E,i=1,2,…,8 p i =E i /E,i=1,2,…,8
其中,
Figure PCTCN2019121414-appb-000012
第一个分量p 1表示最高频段的能量,代表了信号在最高频段范围内能量分布的比例,最后一个分量p n表示信号在最低频段范围内能量分布的比例;根据健康人和CHF患者的归一化的能量分布向量绘制归一化能量分布图,其中横坐标表示分量层次,纵坐标表示归一化的能量分布向量值,曲线表示平均值,误差棒表示标准差;
among them,
Figure PCTCN2019121414-appb-000012
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;
如图8所示,在层次1,健康人能量高于CHF患者能量,随着层次增加,健康人能量逐渐减小;而CHF患者在层次1~3逐渐增加,在层次3达到最高值;当层次大于3时,随着层次增加能量逐渐减小;健康人能量主要集中在层次1~4上,也就是高频率范围内,说明健康人心脏的短时调节能力更强;CHF患者低层次能量相对减低,说明了其短时调节能力降低,而主要的功率集中在中等层次上,在反应心脏节律的层次6上,CHF患者高于健康人,说明CHF患者用在调节心跳节律方面有更高的能量比例。As shown in Figure 8, at level 1, the energy of healthy people is higher than that of patients with CHF. As the level increases, the energy of healthy people gradually decreases; while patients with CHF gradually increase at levels 1 to 3 and reach the highest value at level 3. When the level is greater than 3, the energy gradually decreases as the level increases; the energy of healthy people is mainly concentrated on levels 1 to 4, which is in the high frequency range, indicating that the short-term regulation ability of the heart of healthy people is stronger; the low-level energy of CHF patients The relative decrease indicates that the short-term adjustment ability is reduced, and the main power is concentrated on the middle level. At the level 6 that reflects the heart rhythm, CHF patients are higher than healthy people, indicating that CHF patients have a higher level of regulating heart rhythm. The proportion of energy.
(8)、将CHF患者的ECG信号的归一化的能量分布向量p i和健康人的ECG信号的归一化的能量分布向量p i进行显著性检测得到每一个极值模态函数分量上的概率值P i,当概率值P i小于0.05则表明两者有显著性差异,如表2所示,在层次1、2、3、5、6上二者有显著性差异,CHF患者在分解低层次上能量降低,可能预示着疾病引起心脏在小时间尺度上调节能力下降;而健康人在分解低层次上能量较高,表明健康人心脏具有更好的短时调节能力,对外界环境和身体环境改变有更好的适应能力; (8), the normalized ECG signal CHF patients normalized energy in the energy distribution of the vectors p i and healthy human ECG signal distribution vector p i for a significance test to get on each extremum mode function component the probability value P i, P i when the probability value less than 0.05 indicates a significant difference between the two, as shown in the table, at the level of 1,2,3,5,6 significant difference was found between the two, CHF patients Decrease in energy at low levels of decomposition may indicate that diseases cause a decrease in the heart's ability to regulate on small time scales; while healthy people have higher energy at low levels of decomposition, indicating that healthy people's hearts have better short-term adjustment capabilities and have better short-term adjustment capabilities to the external environment. Have a better ability to adapt to changes in the physical environment;
表2 健康人与CHF患者能量向量T检验Table 2 Energy vector T test of healthy people and CHF patients
Figure PCTCN2019121414-appb-000013
Figure PCTCN2019121414-appb-000013
(9)、在满足P 1和P 2小于0.05的条件下,CHF患者的第一个极值模态函数分量的能量分布向量p 1小于第二个极值模态函数分量的能量分布向量p 2(9) Under the condition that P 1 and P 2 are less than 0.05, the energy distribution vector p 1 of the first extreme value modal function component of CHF patients is smaller than the energy distribution vector p of the second extreme value modal function component 2 .

Claims (6)

  1. 一种基于极值能量分解法的心电图信号分析方法,其特征在于,包括如下步骤:An ECG signal analysis method based on extreme energy decomposition method is characterized in that it comprises the following steps:
    (1)、获取给定时间和给定采样频率下的未知状态的ECG信号x(t);(1) Obtain the ECG signal x(t) in an unknown state at a given time and a given sampling frequency;
    (2)、将ECG信号x(t)进行去噪预处理;预处理的具体方法为:将ECG信号经过40Hz零相位FIR低通滤波器滤波消除高频噪声,然后经过中值滤波器去除基线漂移;(2) Perform denoising preprocessing of the ECG signal x(t); the specific preprocessing method is: pass the ECG signal through a 40Hz zero-phase FIR low-pass filter to remove high-frequency noise, and then pass a median filter to remove the baseline drift;
    (3)、将去噪后的ECG信号x(t)作为原始信号,求出原始信号的所有局部极值点,然后将原始信号的所有极大值点和所有极小值点采用样条曲线连起来分别形成上包络线e max和下包络线e min,得到上、下包络线的包络均值信号m(t)=(e max+e min)/2; (3) Take the denoised ECG signal x(t) as the original signal, find all the local extreme points of the original signal, and then use the spline curve for all the maximum points and all the minimum points of the original signal They are connected to form the upper envelope e max and the lower envelope e min respectively , and the envelope mean signal m(t)=(e max +e min )/2 of the upper and lower envelopes is obtained;
    (4)、将原始信号x(t)减去包络均值信号m(t),得到h(t)=x(t)-m(t);然后判断h(t)是否满足极值模态函数的判定条件,如果不满足,将h(t)作为原始信号返回至步骤(3),直到h k(t)满足极值模态函数的判定条件,则记c 1(t)=h k(t),作为第一个极值模态函数分量; (4) 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;
    (5)、将原始信号x(t)减去第一个极值模态函数分量c 1(t),得到余量r 1(t)=x(t)-c 1(t),然后判断h k(t)是否满足停止准则,如果不满足,将r 1(t)作为新的原始序列x(t),返回至步骤(3)和(4),直到h k(t)满足停止准则,得到第2、3、…、n个极值模态函数分量及余量r n(t),于是将原始信号x(t)分解为n个极值模态函数分量和一个余量,即 (5) 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 (3) and (4) until h k (t) meets the stopping criterion , Get the 2, 3,..., n extremum modal function components and margin r n (t), then decompose the original signal x(t) into n extremum modal function components and a margin, namely
    Figure PCTCN2019121414-appb-100001
    Figure PCTCN2019121414-appb-100001
    (6)、对极值模态函数分量c i(t),i=1,2,…,n,进行频谱分析得到各极值模态函数分量的中心频率; (6) 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;
    (7)、将原始信号x(t)分解得的n个极值模态函数分量,代表了原始信号不同频段的分量,然后计算其各个分量的能量(7) The n extremum modal function components obtained by decomposing the original signal x(t) represent the components of different frequency bands of the original signal, and then calculate the energy of each component
    E i=∫|c i(t)| 2dt,i=1,2,…,n E i =∫|c i (t)| 2 dt,i=1,2,...,n
    将每一个能量值归一化,得到归一化的能量分布向量Normalize each energy value to get a normalized energy distribution vector
    p i=E i/E,i=1,2,…,n p i =E i /E,i=1,2,...,n
    其中,
    Figure PCTCN2019121414-appb-100002
    第一个分量p 1表示最高频段的能量,代表了信号在最高频段范围内能量分布的比例,最后一个分量p n表示信号在最低频段范围内能量分布的比例;根据归一化的能量分布向量绘制归一化能量分布图,其中横坐标表示分量层次,纵坐标表示归一化的能量分布向量值,曲线表示平均值,误差棒表示标准差;
    among them,
    Figure PCTCN2019121414-appb-100002
    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;
    (8)、将未知状态的ECG信号的归一化的能量分布向量p i和标准ECG信号的归一 化的能量分布向量p i进行显著性检测得到每一个极值模态函数分量上的概率值P i,判断第一极值模态函数分量上的概率值P 1和第二极值模态函数分量上的概率值P 2是否小于概率标准值,若不小于则返回步骤(1)重新获取信号; Probability on each extremum mode function component normalization energy distribution vector p i and a significance detector (8), the normalized unknown state ECG signal energy distribution vector p i and the standard ECG signals to obtain Value P i , judge whether the probability value P 1 on the first extreme value modal function component and the probability value P 2 on the second extreme value modal function component are less than the probability standard value, if not, return to step (1) again Get the signal;
    (9)、在满足P 1和P 2小于概率标准值的条件下,若第一个极值模态函数分量的能量分布向量p 1小于第二个极值模态函数分量的能量分布向量p 2,则判定该ECG信号为异常心电图信号。 (9) Under the condition that P 1 and P 2 are less than the probability standard value, if the energy distribution vector p 1 of the first extreme value modal function component is smaller than the energy distribution vector p of the second extreme value modal function component 2 , it is determined that the ECG signal is an abnormal electrocardiogram signal.
  2. 根据权利要求1的一种基于极值能量分解法的心电图信号分析方法,其特征在于:所述原始信号x(t)所需最少数据量N=2 n+1,其中n为分解出的极值模态函数分量的数量。 An ECG signal analysis method based on the extreme value energy decomposition method according to claim 1, characterized in that: the minimum amount of data required for the original signal x(t) is N=2 n+1 , where n is the decomposed extreme The number of components of the value modal function.
  3. 根据权利要求1的一种基于极值能量分解法的心电图信号分析方法,其特征在于:所述步骤(4)中极值模态函数的判定条件为:(a)、在整个数据序列中,极值点的数量与过零点的数量相等或者相差一个;(b)、在任意时刻,上下包络线对于时间轴对称。An ECG 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 (4) is: (a). In the entire data sequence, 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 to the time axis.
  4. 根据权利要求1的一种基于极值能量分解法的心电图信号分析方法,其特征在于:所述步骤(5)中h k(t)满足停止准则的公式为: An electrocardiogram signal analysis method based on extreme energy decomposition method according to claim 1, characterized in that: in the step (5), the formula that h k (t) satisfies the stopping criterion is:
    Figure PCTCN2019121414-appb-100003
    ε表示筛选门限,取0.2~0.3之间。
    Figure PCTCN2019121414-appb-100003
    ε represents the screening threshold, which is between 0.2 and 0.3.
  5. 根据权利要求1的一种基于极值能量分解法的心电图信号分析方法,其特征在于:所述步骤(8)和步骤(9)中的概率标准值为0.05。An ECG signal analysis method based on extreme energy decomposition method according to claim 1, characterized in that: the probability standard value in step (8) and step (9) is 0.05.
  6. 根据权利要求1的一种基于极值能量分解法的心电图信号分析方法,其特征在于:所述步骤(5)中得到的第6个极值模态函数分量波形的重复频率为人体心率,即心动周期。An ECG signal analysis method based on the extreme value energy decomposition method according to claim 1, characterized in that: the repetition frequency of the sixth extreme value modal function component waveform obtained in the step (5) is the human heart rate, namely Cardiac cycle.
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