CoSleepNet: Automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets

Abstract

Sleep relaxes and rests the body by slowing down the metabolism, making us physically stronger and fitter when we wake up. However, in a sleep disorder that may occur in humans, this process is reversed and various dis-orders occur in the body. Therefore, determining sleep stages is vital for diagnosing and treating such sleep disorders. However, manual scoring of sleep stages is tedious, time-consuming and requires considerable expertise. It also suffers from inter-observer variability. Deep learning techniques can automate this process, overcome these problems and produce more consistent results. This study proposes a new hybrid neural network architecture using focal loss and discrete cosine transform methods to solve the training data imbalance problem. The model was trained on four different databases using k-fold validation strategies (subject-wise), and the highest score was 87.11% accuracy, 81.81% Kappa score, and 79.83% MF1 when using two channels (EEG-EOG). The results of our approach are promising when compared to existing methods.