脑电图
睡眠阶段
非快速眼动睡眠
计算机科学
睡眠(系统调用)
人工智能
模式识别(心理学)
样本熵
眼球运动
慢波睡眠
语音识别
信号(编程语言)
多导睡眠图
心理学
神经科学
操作系统
程序设计语言
作者
Rajesh Kumar Tripathy,U. Rajendra Acharya
标识
DOI:10.1016/j.bbe.2018.05.005
摘要
Sleep is a physiological activity and human body restores itself from various diseases during sleep. It is necessary to get sufficient amount of sleep to have sound physiological and mental health. Nowadays, due to our present hectic lifestyle, the amount of sound sleep is reduced. It is very difficult to decipher the various stages of sleep manually. Hence, an automated system may be useful to detect the different stages of sleep. This paper presents a novel method for the classification of sleep stages based on RR-time series and electroencephalogram (EEG) signal. The method uses iterative filtering (IF) based multiresolution analysis approach for the decomposition of RR-time series into intrinsic mode functions (IMFs). The delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ) waves are evaluated from EEG signal using band-pass filtering. The recurrence quantification analysis (RQA) and dispersion entropy (DE) based features are evaluated from the IMFs of RR-time series. The dispersion entropy and the variance features are evaluated from the different bands of EEG signal. The RR-time series features and the EEG features coupled with the deep neural network (DNN) are used for the classification of sleep stages. The simulation results demonstrate that our proposed method has achieved an average accuracy of 85.51%, 94.03% and 95.71% for the classification of ‘sleep vs wake’, ‘light sleep vs deep sleep’ and ‘rapid eye movement (REM) vs non-rapid eye movement (NREM)’ sleep stages.
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