睡眠EEG的多尺度信息熵分析

本文提出一种基于连续小波变换的睡眠EEG分析方法。该方法使用Morlet小波计算EEG信息的小波变换系数，通过计算EEG信号在多个尺度上小波系数的熵分析睡眠EEG。结果表明：浅睡阶段EEG信号的多尺度熵的变化模式与深睡阶段的多尺度熵的变化模式不同，REM睡眠期间EEG信号的多尺度熵的变化与深睡阶段类似，使用多尺度熵可以区分REM睡眠和浅睡时EEG之间的差别。

Analysis of Sleep EEG by Using Multi-Scale Entropy

Based on application of continuous wavelet transform, a new method for analyzing sleep EEG is presented. The wavelet transform coefficients of the EEG signals are computed by using Morlet's wavelet. The information content carried by the wavelet coefficients on any scale is measured with entropy. Analyzing sleep EEG, it may be observed that the change in the multi scale entropy of the EEG signals in light sleep stage is different from that in deep sleep stage, and the change in the multi scale entropy of the EEG signals in REM sleep stage is similar to that in deep sleep. By this method, we can distinguish the characteristics of the EEG in light sleep stage and that in the REM sleep stage.