K-means聚类神经网络分类器在睡眠脑电分期中的应用研究

摘要

Because the sleep stage III and IV approximate entropy (ApEn) values are very close, using the approximate entropy method for sleep staging EEC signal, they are not distinguished. AR model of staging results in stage III and IV EEG signal is established as the EEC signal characteristics.and K-means clustering neural network classifier is used for the sleep phase III and IV stage, so a good stage effect is achieved.%利用近似熵的方法对睡眠 EEG 信号进行分期,但睡眠Ⅲ期和Ⅳ期近似熵值非常接近,靠近似熵值无法区分,对分期结果中的Ⅲ期和Ⅳ期EEG信号进行AR(自回归)建模,作为该段EEG信号的特征属性,利用K-means聚类的神经网络分类器时睡眠Ⅲ期和Ⅳ期进行分期,达到了很好的分期效果.