非快速眼动睡眠
多导睡眠图
计算机科学
人工智能
睡眠阶段
多导睡眠图
脑电图
自编码
睡眠(系统调用)
模式识别(心理学)
人工神经网络
信号(编程语言)
眼球运动
语音识别
心理学
神经科学
操作系统
程序设计语言
作者
Ran Wei,Xinghua Zhang,Jinhai Wang,Xin Dang
标识
DOI:10.1007/s13534-017-0044-1
摘要
The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen’s kappa coefficient of about 0.56 for the classification of Wake, REM and NREM.
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