Deep Learning-Based System for Accurate Sleep Stage Classification and Early Detection of Sleep Disorders Using EEG and EOG Signals

Abstract

Sleep is essential for maintaining overall physical and mental health, yet analyzing sleep patterns manually is a com-plex and time-consuming process that requires expert knowledge and is often prone to subjectivity. In this study, an automated and efficient deep learning-based approach is developed to classify sleep stages using the Sleep-EDF dataset. Physiological signals such as Electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG) are pre-processed and segmented into fixed-length epochs for analysis. These segments are then fed into models including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM architecture, which effectively capture both spatial and temporal features of the signals. The system classifies sleep into stages such as Wake, N1, N2, N3, and REM accurately and robustly. Among the implemented models, the proposed hybrid CNN-LSTM model achieved the best performance with an accuracy of 94.67%. A basic analysis of sleep stage patterns is also performed to observe possible irregularities. Overall, this work presents a scalable solution for sleep stage classification using deep learning techniques.