Automated Sleep Staging Classification System Based on Convolutional Neural Network Using Polysomnography Signals

Sleep is a crucial bodily process that plays a vital role in maintaining overall health and well-being. When diagnosing and treating sleep disorders, the initial step is sleep staging. However, manual sleep staging by physicians can be complicated, leading to a growing interest in computer-aided sleep stage classification algorithms. In this research, a method was introduced for automatically classifying sleep stages by extracting distinctive representations from single-channel EEG signals. PSG signals are selected exclusively for the project because they directly capture the essential physiological changes needed for sleep staging, ensuring both data relevance and quality. This choice also aligns with the project’s goal of feasibility and computational efficiency while avoiding potential ethical and privacy issues linked to audio and video data. Furthermore, it conforms to established practises in the field, ensuring consistency in benchmarking. A filterbank is applied by dividing the range of the frequency signal into two 15 sub epochs. The activity of the signal within distinct frequency ranges during different sleep stages was fully comprehended by computing the standard deviation as a single characteristic from different frequency subbands of the EEG. These characteristics served as the input for a two-stream convolutional neural network (CNN) that was trained using a two-stage learning methodology for classification. These characteristics served as the input for a two-stream convolutional neural network (CNN) that was trained using a two-stage learning methodology for classification.