Automatic Sleep Stage Identification with Time Distributed Convolutional Neural Network

Abstract

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.