Li Gu, Fan Yingle, Pang Quan
Wenthou Medical College, Wenzhou 325000, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2009 Aug;26(4):869-72.
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%.
本文提出了一种基于脑电图排列熵的自动睡眠阶段分类新方法。脑电图排列熵在睡眠的各个阶段有显著差异，并呈现出规律变化的趋势。因此，它可以用作各阶段睡眠脑电图的特征。采用最近邻法作为模式识别方法对睡眠阶段进行分类。对750个睡眠脑电图样本进行了实验，平均识别率可达79.6%。
