短时高频心电图的基本尺度熵分析

The Base-scale Entropy Analysis for Short-term High Frequency Eletrocardiogram Signals

采用心电信号对心脏活动进行检测和分析是医学临床实践中心脏功能检测和诊断的最重要的方法和手段。本研究提出一种基本尺度熵方法来探讨时间序列的复杂性。该方法不受波形模式的影响,可以用来分析任意波形模式的信号,并且在叠加白噪声后依然显示方法的有效性。对于低维的混沌Lorentz序列,基本尺度熵值可以敏感的捕捉到动力学系统的复杂性的变化。将该方法用于心电图信号的分析,可有效地从短时序列中区分出不同的生理、病理信号。该方法简单、运算快速、抗干扰,能够给临床应用提供方便。

It is an important method for using eletrocardiogram to detect and diagnose heart funtion in clinical practice of medicine. We introduce a as a base-scale entropy complexity measure for analyzing time series. This measure is not affected by wave mode, which can be directly applied to arbitrary real-world data and is especially effective in the presence of random Gaussian noise. For the well-known chaotic dynamical system-Lorenz system, the base-scale entropy can effectively catch its complexity change. This measure was applied to the human physiologic time series-high frequency eletrocardiogram signal （HFECG）. The results showed that this measure can effectively detect the complexity dissimilarity of physiologic time series in different physiologic/pathologic states. Besides, the method is simple, easily calculated and has some antinoise ability, which is feasible for clinical applications.