基于排列组合熵的脑电信号睡眠分期研究

A Study of Sleep Stage Classification Based on Permutation Entropy for Electroencephalogram

提出了一种基于脑电信号排列组合熵的睡眠自动分期方法。由于排列组合熵在睡眠各个阶段具有显著差异,并呈现出规律性的变换趋势,因此将排列组合熵的大小作为睡眠脑电信号各个时期的特征,最终利用最近邻模式分类方法对睡眠各阶段进行分期决策。通过对750个睡眠脑电信号样本进行分期,平均正确率达到79.6%。

This paper presents a new method for automatic sleep stage classification which is based on the EEG permutation entropy. The EEG permutation entropy has notable distinction in each stage of sleep and manifests the trend of regular transforming. So it can be used as features of sleep EEG in each stage. Nearest neighbor is employed as the pattern recognition method to classify the stages of sleep. Experiments are conducted on 750 sleep EEG samples and the mean identification rate can be up to 79.6%.