Automatic Analysis of Arrhythmia Based on HRV Nonlinear Characteristics
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摘要: 心率变异性(HRV)信号包含大量心脏和心血管系统的生理和病理信息,对其进行深入分析可以帮助诊断和预警心律不齐等心脏疾病.论文利用MIT-BIH心电数据库,提取正常心律和心律不齐两种心电数据并进行信号预处理以消除噪声干扰;采用小波变换提取小波系数的模极值和过零点以得到心电R波信号,计算其一阶差分得到HRV序列.然后,提取HRV信号的小波熵、近似熵和基本尺度熵3种非线性特征,并对正常心电和心律不齐心电特征进行差异统计检验.仿真结果表明,HRV信号的非线性特征可以有效地识别正常心律和心律不齐心电信号.Abstract: Heart rate variability（HRV） signals contain a large amount of physiological and pathological information of the heart and cardiovascular system, and in-depth analysis of them can help diagnose and warn of heart diseases such as arrhythmia. The paper uses the MIT-BIH ECG database to extract two types of ECG data of normal heart rhythm and arrhythmia, and performs signal preprocessing to eliminate noise interference; the wavelet transform is used to extract the modulus extreme value and zero-crossing point of wavelet coefficients to obtain the ECG R wave Signal.The HRV sequence is obtained by the first-order difference of ECG R-wave signal.The three nonlinear characteristics of wavelet entropy, approximate entropy, and basic scale entropy of the HRV signal are extracted, and the difference between normal ECG and arrhythmia ECG characteristics is statistically tested. The simulation results show that the nonlinear characteristics of the HRV signal can identify the normal heart rhythm and arrhythmia ECG signals effectively.
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Keywords:
- ECG signal /
- wavelet transform /
- heart rate variability /
- nonlinear analysis /
- arrhythmia
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