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Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals

计算机科学 脑电图 人工智能 非快速眼动睡眠 循环神经网络 睡眠(系统调用) 睡眠阶段 神经认知 特征(语言学) 模式识别(心理学) 任务(项目管理) 人工神经网络 语音识别 机器学习 多导睡眠图 认知 眼球运动 心理学 神经科学 经济 管理 哲学 操作系统 语言学
作者
Nicola Michielli,U. Rajendra Acharya,Filippo Molinari
出处
期刊:Computers in Biology and Medicine [Elsevier]
卷期号:106: 71-81 被引量:274
标识
DOI:10.1016/j.compbiomed.2019.01.013
摘要

Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the mental/cognitive human capacity in performing a specific task. It is difficult to develop the study protocols as the subject's NCP changes in a known predictable way. Sleep is time-varying NCP and can be used to develop novel NCP techniques. Accurate analysis and interpretation of human sleep electroencephalographic (EEG) signals is needed for proper NCP assessment. In addition, sleep deprivation may cause prominent cognitive risks in performing many common activities such as driving or controlling a generic device; therefore, sleep scoring is a crucial part of the process. In the sleep cycle, the first stage of non-rapid eye movement (NREM) sleep or stage N1 is the transition between wakefulness and drowsiness and becomes relevant for the study of NCP. In this study, a novel cascaded recurrent neural network (RNN) architecture based on long short-term memory (LSTM) blocks, is proposed for the automated scoring of sleep stages using EEG signals derived from a single-channel. Fifty-five time and frequency-domain features were extracted from the EEG signals and fed to feature reduction algorithms to select the most relevant ones. The selected features constituted as the inputs to the LSTM networks. The cascaded architecture is composed of two LSTM RNNs: the first network performed 4-class classification (i.e. the five sleep stages with the merging of stages N1 and REM into a single stage) with a classification rate of 90.8%, and the second one obtained a recognition performance of 83.6% for 2-class classification (i.e. N1 vs REM). The overall percentage of correct classification for five sleep stages is found to be 86.7%. The objective of this work is to improve classification performance in sleep stage N1, as a first step of NCP assessment, and at the same time obtain satisfactory classification results in the other sleep stages.
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