计算机科学
卷积神经网络
睡眠阶段
脑电图
眼电学
睡眠(系统调用)
多导睡眠图
人工智能
鉴定(生物学)
信号(编程语言)
模式识别(心理学)
机器学习
语音识别
眼球运动
心理学
神经科学
植物
程序设计语言
生物
操作系统
作者
Micheal Dut,Morten Goodwin,Christian W. Omlin
标识
DOI:10.1109/ijcnn52387.2021.9533542
摘要
Polysomnography (PSG), the gold standard for sleep stage classification, requires a sleep expert for scoring and is both resource-intensive and expensive. Many researchers currently focus on the real-time classification of the sleep stages based on biomedical signals, such as Electroencephalograph (EEG) and electrooculography (EOG). However, most of the research work is based on machine learning models with multiple signal inputs or hand-engineered features requiring prior knowledge of the sleep domain. We propose a novel encoded Time-Distributed Convolutional Neural Network (TDConvNet) to automatically classify sleep stages based on a single raw PSG signal. The TDConvNet can infer sleep stages in just 30-second epochs for the 5-stage sleep classification using a single EEG or EOG signal from the open Sleep-EDF dataset. We evaluated the TDConvNet performance on EEGs and EOG signals. The evaluation results show that the proposed method achieved the best performance with the EEG Fpz-Cz signal (0.85) compared to current literature, followed by EEG Pz-Oz (0.84) and EOG horizontal (0.82). The source code is available at https://github.com/michealdutt/TDConvNet.
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