基于AR模型的过速型室性心律失常的短时心率变异性预测分析

目的:基于短时(5 min)心率变异信号的频域分析,探究过速型室性心律失常[包括室性心动过速(ventricular tachycardia,VT)和室颤(ventricular fibrillation,VF)]的发生机理及有效预测方法。方法:以78名患者体内心脏复律除颤器记录的VT/VF事件发生前5 min的RR间期序列(作为VT/VF样本组)和来自同一患者的正常窦性节律(作为正常对照组)构成的样本对为分析序列,对分析序列预处理后,基于AR模型方法计算其功率谱,提取相应的频谱能量特征,并进行统计分析。结果:与正常对照组序列相比较,VT/VF样本组的功率谱总功率、甚低频功率、低频功率、高频功率及低频功率峰值显著降低,统计检验概率分别为P<0.003、P<0.003、P<0.002、P<0.005、P<0.002。结论:研究表明VT/VF发生前患者的心率变异性明显降低,说明其自主神经系统调节功能受损和失衡,频谱能量特征的改变或许是VT/VF发生的某种生理预兆,对于短时预报恶性室性心律失常有重要意义。

Analysis of Electroencephalogram Based on Wavelet Spectrum and Wavelet Entropy

Based on discrete wavelet transform, both relative wavelet energy （RWE） and segment wavelet entropy （SWE） of electroencephalogram （EEG） are defined in this paper. The RWE provides quantitatively the information about the relative energy associated with different frequency bands present in the EEG. The SWE carries information about the degree of order or disorder associated with different time segment of EEG evolution, which can determine the time-segment loealizations of abnormal dynamic processes of brain activity due to the localization characteristics of the wavelet transform. The experimental results show that the RWE and SWE are different between epileptic EEGs and normal EEGs, which demonstrate that the RWE and the SWE are helpful to analyze the dynamic behavior of different EEGs.