摘要
比较了样本熵与近似熵算法的区别,通过对构造的一个由随机信号和确定性信号组成的混合系统进行分析可以看出在公差阈值小于0.2时,样本熵比近似熵更适合于时间序列信号的复杂度分析。然后,对采集的整夜睡眠脑电信号,用样本熵作为睡眠脑电数据的特征值,分析了睡眠过程不同阶段的实验数据。结果表明,不同睡眠时期样本熵有差别,随睡眠深度的加深,样本熵值变小。因此,样本熵可以很好地区分不同睡眠时期并作为睡眠自动分期的一个重要的非线性特征参数。
Compared sample entropy algorithm with approximate entropy algorithm. The two kinds of entropy analysis of a mixture system including stochastic signal and deterministic signal show that sample entropy is better than approximate entropy in time-series complexity analysis when the tolerance threshold lower than 0. 2 requirement. And then, the experiment data of different sleep stages acquired from one night sleep EEG signal was analyzed using sample entropy as a characteristic value. It is clearly that the sample entropy of different sleep stages is difference, the deeper the sleep the smaller the sample entropy. Therefore, sample entropy can divide sleep stages well and can be an important nonlinearity characteristic parameter in sleep automatic stage.
出处
《电子器件》
CAS
2008年第3期972-975,共4页
Chinese Journal of Electron Devices
基金
天津市科技发展计划资助项目(043102111)
关键词
睡眠
近似熵
样本熵
脑电(EEG)
非线性
sleep
approximate entropy
sample entropy
ElectroEncephaloGraph（EEG）
nonlinearity
