基于代替数据法的心率变异信号混沌特性的识别

Chaotic Identification of HRV Based on Surrogate Data Method

采用代替数据法对心率变异(Heart rate variability,HRV)信号的混沌特性进行识别。首先介绍了利用一步预测误差平均绝对值(MAE)为统计量的基于代替数据法的混沌识别原理,用一个已知的混沌系统响应和一个有色噪声信号证实该算法的有效性,然后对几组典型生理和病理状态的HRV进行分析,计算并比较了几个特征参数的数值变化情况。

The chaos of heart rate variance（HRV） is identified by surrogate data method in this paper. Firstly, the main principle of chaotic identification is introduced, in which the median absolute error （MAE） of one-step prediction for HRV is set as the statistic. The algorithm is checked with a known chaotic system response and a colored noise signal. Then, some typical healthy and unhealthy HRVs are analyzed with the method of surrogate data, and some characteristic parameters from this method are compared.