心电波形检测与心率变异性分析方法研究
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摘要
心电信号和心率变异信号蕴含着丰富的心脏及其神经系统活动信息，对这些信息的提取和处理在心脏疾病的预防、诊断和治疗等方面具有重要的意义。论文在前人研究成果的基础上，开展了心电信号预处理、心电特征波检测及心率变异性分析的研究工作。
本文简要阐述了ECG信号及HRV信号的生理机理、典型特征信息及其临床诊断意义，综述了目前心电信号预处理、心电特征波形识别及特征信息提取、HRV信号分析的国内外研究现状。针对典型的ECG和HRV分析方法存在的一些不足，采用新的信号分析手段，在改进和创新ECG和HRV分析方法上进行了研究尝试，取得了一些有价值的研究成果，对于开发研制新型心电分析系统有重要意义。论文主要的研究成果包括以下几个方面：
1）针对小波变换的阀值去噪易在心电信号的Q、S波处出现Gibbs振荡现象及消除基线漂移时可能导致T波、ST段波形形态失真的问题，提出了将平稳小波变换和自适应滤波方法相结合的一次性ECG消噪方法。算法在低尺度分量上通过阈值去噪方法滤除心电的工频和肌电等干扰，在高尺度分量上引入自适应滤波算法滤除基线漂移，重构后很好地保持了ECG的波形形态，达到了一次性消噪的目的。仿真实验表明算法有效地克服了Gibbs震荡现象，降低了心电信号T波、ST段波形滤波后的失真度，为提取准确的心电特征信息奠定了基础。
2）提出了非线性能量算子和小波分解相结合的R波检测算法。该算法采用Marr小波为基本小波，通过检测3尺度分量的极值点标定R波的位置，既有效地抑制了噪声干扰的影响，又克服了传统样条小波变换R波检测需定位过零点的不便。对3尺度分量引入平滑非线性能量算子运算，突出了R波尖峰，抑制了高大T波、大P波对R检测的影响，与传统小波变换的R波检测算法相比，算法的检测率高、抗干扰能力强、计算量小、实时性好，对MIT-BIH的心律失常数据库的实际心电数据的仿真实验表明了算法的有效性。
3）提出了基于Hilbert时频谱的HRV信号的时频分析方法。通过分析不同生理病理状况下HRV信号的Hilbert时频谱的差异，依据短时程HRV信号的频域指标，在不同生理频带上绘制Hilbert能量棒形图，提取了各频带能量特征作为定量评价心率变异性的时频特征，分析了心率改变和时频特征的相关性，指出心率改变是HRV信号分析应考虑的一个重要因素。通过对年轻人和老年人、健康人和心衰病人样本数据的统计分析表明提取的时频特征有很好的区分性能，准确地反映了心脏的交感和迷走神经系统的调控作用及受损状况。
4）采用改进的HHT分析方法对室速或室颤事件的发生进行预测分析。首先，将心脏复律除颤器存储的室速或室颤事件发作前的RR间期序列变换为瞬时心率信号，利用小波包变换将其预分解为几个窄带信号，然后对各窄带分量信号进行EMD分解，通过相关性阈值判别剔除虚假模态分量后获取瞬时心率信号的Hilbert边际谱，并提取各生理频段幅值特征作为室速或室颤事件预测特征。仿真实验表明室速或室颤事件发作前瞬时心率信号的高频、甚高频幅值及总幅值较正常窦性心率信号显著升高，而低高频幅值比显著降低。提示室速或室颤事件发作前心脏交感和迷走神经活动均有所增强，以迷走神经活动增强更为显著，引起心脏自主神经系统调节失衡。统计分析结果显示改进的HHT方法谱特征的区分性能明显优于传统的HHT方法。
5）提出了HHT的边际谱熵及能量谱熵的概念及分析方法。通过对混有不同程度白噪声的常规信号和混沌时间序列复杂性的分析，显示出边际谱熵和能量谱熵在刻画信号复杂度变化、抗脉冲干扰性能优于传统复杂度和熵分析方法，应用于年轻人、老年人及房颤病人的HRV信号分析，两种频域谱熵能从HRV信号中敏感地检测出生理和病理状态的变化，分析性能优于传统的功率谱熵的方法。
6）提出了基于分频段Hilbert时频谱熵的HRV信号的分析方法。通过提取不同生理频段的Hilbert谱熵作为心率变异性的特征评价指标。Hilbert谱熵特征反映了HRV信号能量在时频域分布的不确定性，而按频段适当分离各种生理因素的Hilbert谱熵和不同的生理病理机理联系紧密，有利于表征心脏自主神经系统的调控规律。通过对年轻人、老年人及房颤病人和健康人、充血性心力衰竭病人的HRV信号的仿真分析表明，算法有效地区分了各样本组，准确反映了不同生理病理状况下的心脏交感神经和迷走神经调节的变化规律。
论文最后对课题的研究工作进行了总结，并对今后研究提出了设想。
A large amount of information about heart function and its nervous activity is containedin ECG and HRV signal. Therefore, it is greatly significant to extract and process theinformation for the prevention, diagnosis and therapy etc. of heart disease. The thesis mainlydiscusses the ECG preprocessing, feature analysis and heart rate variability, which is based onthe predecessors’ research results and literature material.
The physiologic principle and mechanism, typical feature and clinical diagnostic value ofECG and HRV signal are briefly illustrated in this thesis; the present developing status ofpreprocess methods of ECG signal and the methods of feature waveform recognition of ECGsignal and HRV analysis methods are summarized. According to the existing deficiencies ofthe typical analysis algorithm, some studies have been tried to improve and innovate theanalysis methods of ECG and HRV signal based on the new signal analysis methods, andsome research outcomes have been obtained, which turns out to be significant to develop newECG automatic analysis system. It mainly includes the following research works:
1) As to the de-noising method of wavelet threshold, Gibbs oscillatory phenomena easilyoccurs in Q,S waveform of ECG signal and the serious distortion for T waveform and STsegment waveform of ECG signal can be easily caused when baseline drift is eliminated. Tosolve this problem, the one-off denoising method of ECG signal combining stationary wavelettransform and adaptive filter is proposed. The power-line interference and EMG noise iseliminated based on the de-noising method of wavelet threshold in low scale decompositioncomponent, and the adaptive filter is introduced so as to eliminate baseline drift in large scaledecomposition component,the ECG waveform form remain well after the reconstruction, theone-off denoising is achieved. Simulation and comparison test result shows that thissynthetical filter can avoid Gibbs oscillatory phenomena effectively and reduce the distortionfor T waveform and ST segment waveform of ECG signal, which laid foundation for accuratefeature information extraction of ECG signal.
2) The combination algorithm of wavelet transform and nonlinear energy operator isproposed for R waveform detection of ECG signal, which uses Marr wavelet as wavelet basis.Through demarcating the location of R waveform by the extreme value point on3scalecomponent of wavelet decomposition, the effect of noise is effectively restrains,and thedisadvantage that the location of zero-crossings are needed for R waveform detection byspline wavelet transform is overcome. Smooth nonlinear energy operator is operated on3scale component so as to protrude R waveform peak and suppress high T waveform and Pwaveform. In comparison with the R waveform detection method of traditional wavelet transform, the method shows high detection rate, better ability to anti-interference, much lesscomputation and good real-time performance. Simulation test shows that the method iseffective for actual ECG data in the MIT/BIH arrhythmia data.
3) A time-frequency analysis method is proposed for HRV signal based on Hilberttime-frequency spectrum. The method analyzes the difference of Hilbert time-frequencyspectrum for HRV signal under different physiological or pathological conditions, and theHilbert energy bar chart of HRV signal is plotted in the different physiological frequency bandby the characteristic index of short-time HRV signal in the linear frequency domain,theenergy features are extracted in the different physiological frequency band as thetime-frequency feature of quantitative evaluating for heart rate variability, the correlationbetween the heart rate change and the time-frequency features is studied in this paper, andpoints out that the change of heart rate is a important factor considered in HRV signalanalysis.Simulation tests to HRV signal of the young, old and heart failure patients show thatthe new time-frequency features have good differentiating performance, and can accuratelyreflect the regulatory function and damage of sympathetic and vagal nervous system of heart.
4) The ventricular tachycardia (VT) and ventricular fibrillation (VF) event is predictedbased on improved HHT method. Firstly, the RR interval series preceding the onset of VT/VFevents is transformed instantaneous heart rate (IHR) series, and it is pre-decomposed into aseries of narrow-bands signal by wavelet packet transform, then EMD decomposition iscarried out for each narrow-band component, the undesirable components are removed by thecorrelation threshold identification, the new intrinsic mode function (IMF) components andHilbert marginal spectrum of IHR series are obtained, the amplitude features of differentphysiological frequency band are extracted to be used for prediction features of VT/VF events.Simulation test shows that high frequency amplitude, very high frequency amplitude and totalamplitude of IHR series preceding the onset of VT/VF are significantly higher than that ofnormal sinus rhythm, and low-to-high frequency amplitude ratio is significantly lower thanthat of normal sinus rhythm. It suggests that the activity of sympathetic and vagal nerveappears to increase preceding the onset of VT/VF event, and the activity of vagal nerve ismore marked, which causes imbalance of the sympathetic and vagal nerve activity.Simulation test is carried out for the improved HHT method and the traditional HHT, thestatistical analysis show that the differentiated performance of the improved HHT is morebetter than that of traditional HHT.
5) The conception of HHT marginal spectrum entropy and energy spectrum entropy andthe analysis method of HRV signal are proposed. The complexity analysis is processed for theconventional signal with some degree of white noise and chaotic time series. The results show that the method is superior to the analysis method of the traditional complexity and entropy indepicting signal complexity and anti-pulse interference. Applying the new approach to HRVsignal of the young, older and atrial fibrillation patients, the results show that the two entropyof frequency domain can sensitively detect the physiological and pathological changes fromHRV signal, and its analytical performace is better than that of the traditional power spectrumentropy method.
6) The new method of HRV signal analysis is proposed based on the Hilbert spectrumentropy dividing frequency range. the Hilbert spectrum entropy of HRV signal in differentfrequency range is calculated, which is used as the characteristic evaluation index of heart ratevariability. The Hilbert spectrum entropy can reflect the uncertainty of HRV signal energy ontime-frequency distributions, the Hilbert spectrum entropy features of HRV signal based onappropriate separation for various physiological factors by dividing frequency range are moreclosely linked to different physiological and pathological functions and more conducive tomanifest the regulation law of autonomic nervous system of heart. The results of thesimulation test to HRV signal of the sample group for the young, elder and patients with atrialfibrillation, and the sample group for health person and congestive heart failure(CHF) patientsshow that this method can effectively differentiate different sample group, and can accuratelyreflect the regulation law of sympathetic and vagal nervous system of heart in differentphysiological and pathological functions.
At last, the dissertation sums up the research of this project and makes the expectationabout the development direction of the assignment.
本文简要阐述了ECG信号及HRV信号的生理机理、典型特征信息及其临床诊断意义，综述了目前心电信号预处理、心电特征波形识别及特征信息提取、HRV信号分析的国内外研究现状。针对典型的ECG和HRV分析方法存在的一些不足，采用新的信号分析手段，在改进和创新ECG和HRV分析方法上进行了研究尝试，取得了一些有价值的研究成果，对于开发研制新型心电分析系统有重要意义。论文主要的研究成果包括以下几个方面：
1）针对小波变换的阀值去噪易在心电信号的Q、S波处出现Gibbs振荡现象及消除基线漂移时可能导致T波、ST段波形形态失真的问题，提出了将平稳小波变换和自适应滤波方法相结合的一次性ECG消噪方法。算法在低尺度分量上通过阈值去噪方法滤除心电的工频和肌电等干扰，在高尺度分量上引入自适应滤波算法滤除基线漂移，重构后很好地保持了ECG的波形形态，达到了一次性消噪的目的。仿真实验表明算法有效地克服了Gibbs震荡现象，降低了心电信号T波、ST段波形滤波后的失真度，为提取准确的心电特征信息奠定了基础。
2）提出了非线性能量算子和小波分解相结合的R波检测算法。该算法采用Marr小波为基本小波，通过检测3尺度分量的极值点标定R波的位置，既有效地抑制了噪声干扰的影响，又克服了传统样条小波变换R波检测需定位过零点的不便。对3尺度分量引入平滑非线性能量算子运算，突出了R波尖峰，抑制了高大T波、大P波对R检测的影响，与传统小波变换的R波检测算法相比，算法的检测率高、抗干扰能力强、计算量小、实时性好，对MIT-BIH的心律失常数据库的实际心电数据的仿真实验表明了算法的有效性。
3）提出了基于Hilbert时频谱的HRV信号的时频分析方法。通过分析不同生理病理状况下HRV信号的Hilbert时频谱的差异，依据短时程HRV信号的频域指标，在不同生理频带上绘制Hilbert能量棒形图，提取了各频带能量特征作为定量评价心率变异性的时频特征，分析了心率改变和时频特征的相关性，指出心率改变是HRV信号分析应考虑的一个重要因素。通过对年轻人和老年人、健康人和心衰病人样本数据的统计分析表明提取的时频特征有很好的区分性能，准确地反映了心脏的交感和迷走神经系统的调控作用及受损状况。
4）采用改进的HHT分析方法对室速或室颤事件的发生进行预测分析。首先，将心脏复律除颤器存储的室速或室颤事件发作前的RR间期序列变换为瞬时心率信号，利用小波包变换将其预分解为几个窄带信号，然后对各窄带分量信号进行EMD分解，通过相关性阈值判别剔除虚假模态分量后获取瞬时心率信号的Hilbert边际谱，并提取各生理频段幅值特征作为室速或室颤事件预测特征。仿真实验表明室速或室颤事件发作前瞬时心率信号的高频、甚高频幅值及总幅值较正常窦性心率信号显著升高，而低高频幅值比显著降低。提示室速或室颤事件发作前心脏交感和迷走神经活动均有所增强，以迷走神经活动增强更为显著，引起心脏自主神经系统调节失衡。统计分析结果显示改进的HHT方法谱特征的区分性能明显优于传统的HHT方法。
5）提出了HHT的边际谱熵及能量谱熵的概念及分析方法。通过对混有不同程度白噪声的常规信号和混沌时间序列复杂性的分析，显示出边际谱熵和能量谱熵在刻画信号复杂度变化、抗脉冲干扰性能优于传统复杂度和熵分析方法，应用于年轻人、老年人及房颤病人的HRV信号分析，两种频域谱熵能从HRV信号中敏感地检测出生理和病理状态的变化，分析性能优于传统的功率谱熵的方法。
6）提出了基于分频段Hilbert时频谱熵的HRV信号的分析方法。通过提取不同生理频段的Hilbert谱熵作为心率变异性的特征评价指标。Hilbert谱熵特征反映了HRV信号能量在时频域分布的不确定性，而按频段适当分离各种生理因素的Hilbert谱熵和不同的生理病理机理联系紧密，有利于表征心脏自主神经系统的调控规律。通过对年轻人、老年人及房颤病人和健康人、充血性心力衰竭病人的HRV信号的仿真分析表明，算法有效地区分了各样本组，准确反映了不同生理病理状况下的心脏交感神经和迷走神经调节的变化规律。
论文最后对课题的研究工作进行了总结，并对今后研究提出了设想。
A large amount of information about heart function and its nervous activity is containedin ECG and HRV signal. Therefore, it is greatly significant to extract and process theinformation for the prevention, diagnosis and therapy etc. of heart disease. The thesis mainlydiscusses the ECG preprocessing, feature analysis and heart rate variability, which is based onthe predecessors’ research results and literature material.
The physiologic principle and mechanism, typical feature and clinical diagnostic value ofECG and HRV signal are briefly illustrated in this thesis; the present developing status ofpreprocess methods of ECG signal and the methods of feature waveform recognition of ECGsignal and HRV analysis methods are summarized. According to the existing deficiencies ofthe typical analysis algorithm, some studies have been tried to improve and innovate theanalysis methods of ECG and HRV signal based on the new signal analysis methods, andsome research outcomes have been obtained, which turns out to be significant to develop newECG automatic analysis system. It mainly includes the following research works:
1) As to the de-noising method of wavelet threshold, Gibbs oscillatory phenomena easilyoccurs in Q,S waveform of ECG signal and the serious distortion for T waveform and STsegment waveform of ECG signal can be easily caused when baseline drift is eliminated. Tosolve this problem, the one-off denoising method of ECG signal combining stationary wavelettransform and adaptive filter is proposed. The power-line interference and EMG noise iseliminated based on the de-noising method of wavelet threshold in low scale decompositioncomponent, and the adaptive filter is introduced so as to eliminate baseline drift in large scaledecomposition component,the ECG waveform form remain well after the reconstruction, theone-off denoising is achieved. Simulation and comparison test result shows that thissynthetical filter can avoid Gibbs oscillatory phenomena effectively and reduce the distortionfor T waveform and ST segment waveform of ECG signal, which laid foundation for accuratefeature information extraction of ECG signal.
2) The combination algorithm of wavelet transform and nonlinear energy operator isproposed for R waveform detection of ECG signal, which uses Marr wavelet as wavelet basis.Through demarcating the location of R waveform by the extreme value point on3scalecomponent of wavelet decomposition, the effect of noise is effectively restrains,and thedisadvantage that the location of zero-crossings are needed for R waveform detection byspline wavelet transform is overcome. Smooth nonlinear energy operator is operated on3scale component so as to protrude R waveform peak and suppress high T waveform and Pwaveform. In comparison with the R waveform detection method of traditional wavelet transform, the method shows high detection rate, better ability to anti-interference, much lesscomputation and good real-time performance. Simulation test shows that the method iseffective for actual ECG data in the MIT/BIH arrhythmia data.
3) A time-frequency analysis method is proposed for HRV signal based on Hilberttime-frequency spectrum. The method analyzes the difference of Hilbert time-frequencyspectrum for HRV signal under different physiological or pathological conditions, and theHilbert energy bar chart of HRV signal is plotted in the different physiological frequency bandby the characteristic index of short-time HRV signal in the linear frequency domain,theenergy features are extracted in the different physiological frequency band as thetime-frequency feature of quantitative evaluating for heart rate variability, the correlationbetween the heart rate change and the time-frequency features is studied in this paper, andpoints out that the change of heart rate is a important factor considered in HRV signalanalysis.Simulation tests to HRV signal of the young, old and heart failure patients show thatthe new time-frequency features have good differentiating performance, and can accuratelyreflect the regulatory function and damage of sympathetic and vagal nervous system of heart.
4) The ventricular tachycardia (VT) and ventricular fibrillation (VF) event is predictedbased on improved HHT method. Firstly, the RR interval series preceding the onset of VT/VFevents is transformed instantaneous heart rate (IHR) series, and it is pre-decomposed into aseries of narrow-bands signal by wavelet packet transform, then EMD decomposition iscarried out for each narrow-band component, the undesirable components are removed by thecorrelation threshold identification, the new intrinsic mode function (IMF) components andHilbert marginal spectrum of IHR series are obtained, the amplitude features of differentphysiological frequency band are extracted to be used for prediction features of VT/VF events.Simulation test shows that high frequency amplitude, very high frequency amplitude and totalamplitude of IHR series preceding the onset of VT/VF are significantly higher than that ofnormal sinus rhythm, and low-to-high frequency amplitude ratio is significantly lower thanthat of normal sinus rhythm. It suggests that the activity of sympathetic and vagal nerveappears to increase preceding the onset of VT/VF event, and the activity of vagal nerve ismore marked, which causes imbalance of the sympathetic and vagal nerve activity.Simulation test is carried out for the improved HHT method and the traditional HHT, thestatistical analysis show that the differentiated performance of the improved HHT is morebetter than that of traditional HHT.
5) The conception of HHT marginal spectrum entropy and energy spectrum entropy andthe analysis method of HRV signal are proposed. The complexity analysis is processed for theconventional signal with some degree of white noise and chaotic time series. The results show that the method is superior to the analysis method of the traditional complexity and entropy indepicting signal complexity and anti-pulse interference. Applying the new approach to HRVsignal of the young, older and atrial fibrillation patients, the results show that the two entropyof frequency domain can sensitively detect the physiological and pathological changes fromHRV signal, and its analytical performace is better than that of the traditional power spectrumentropy method.
6) The new method of HRV signal analysis is proposed based on the Hilbert spectrumentropy dividing frequency range. the Hilbert spectrum entropy of HRV signal in differentfrequency range is calculated, which is used as the characteristic evaluation index of heart ratevariability. The Hilbert spectrum entropy can reflect the uncertainty of HRV signal energy ontime-frequency distributions, the Hilbert spectrum entropy features of HRV signal based onappropriate separation for various physiological factors by dividing frequency range are moreclosely linked to different physiological and pathological functions and more conducive tomanifest the regulation law of autonomic nervous system of heart. The results of thesimulation test to HRV signal of the sample group for the young, elder and patients with atrialfibrillation, and the sample group for health person and congestive heart failure(CHF) patientsshow that this method can effectively differentiate different sample group, and can accuratelyreflect the regulation law of sympathetic and vagal nervous system of heart in differentphysiological and pathological functions.
At last, the dissertation sums up the research of this project and makes the expectationabout the development direction of the assignment.
引文
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