摘要： 在心率变异性(HRV)数据的短时非线性分析中，单纯的样本熵算法不能有效提取健康人和充血性心衰(CHF)患者的信号特征差异。为此，提出一种基于Teager 能量算子的样本熵分析算法。采用Teager算子的预处理捕获心脏动力学活动中的异常节律变化，以强化样本熵分析效果。对MIT-BIH数据库中样本的实验结果表明，该算法可反映健康人与CHF患者的短时HRV信号非线性复杂性的差异，为计算机分析诊断心衰等疾病提供新的辅助依据。
Abstract: The original sample entropy algorithm can not effectively extract the characteristic difference of Heart Rate Variability(HRV) signals between healthy people and Congestive Heart Failure(CHF) sufferers in short-term nonlinear analysis. This paper presents an improved algorithm of sample entropy based on the Teager Energy Operator(TEO). The heartbeat interval series are preprocessed with TEO to detect the abnormal rhythm in the activity of cardiac dynamics, in order to strengthen the effect of sample entropy analysis. Experimental results of signals from healthy people and CHF sufferers in MIT-BIH database show that this algorithm can significantly reflect the difference of nonlinear complexity between CHF sufferers and healthy people’s short-term HRV signals, and thus can be used as a new auxiliary basis for computer diagnosis of heart failure.
Heart Rate Variability(HRV),
Teager energy operator,
congestive heart failure