Sleep Stage Identification based on Single-Channel EEG Signals using 1-D Convolutional Autoencoders

Automatic sleep stage classification can play a vital role when measuring sleep quality and diagnosing different sleep-related ailments. Several automated sleep stage identification algorithms have been proposed using various physiological signals. However, most of these methods use hand-crafted features or multiple Electroencephalography (EEG) signals. This work proposes a one-dimensional convolutional autoencoder (1D-CAE) based on a single-channel EEG signal for sleep stage identification. A total of five 1-D CAEs models are implemented, and each model is trained to reconstructs a specific sleep stage with the lowest reconstruction error, thus enabling the sleep stage identification based on this error. Furthermore, the proposed approach is evaluated on the Sleep EDF expanded datasets and achieved an overall classification accuracy of 87.2% using a single-channel EEG FPz-Cz signal. Also, our approach demonstrated the highest sleep stage identification accuracy compared with the recent algorithms, especially for sleep stage N1, a short period that transitions between sleep stages during a sleep cycle.