Sleep Apnea and Rapid Eye Movement Detection using ResNet-50 and Gradient Boost

Abstract

Sleep apnea is a prevalent sleep problem marked by interruptions in breathing or superficial breaths while asleep. This frequently results in disrupted sleep patterns and can pose significant health risks such as cardiovascular issues and daytime exhaustion Rapid Eye Movement (REM) sleep stage is easily identifiable due to rapid eye movements, intense dreaming, and muscle immobility. This stage is vital for cognitive processes, the strengthening of memories, and the regulation of emotions. Detection of REM sleep is essential for understanding sleep architecture and diagnosing various sleep disorders. This paper proposes two machine learning models to detect these disorders from physiological signals. The study employs the Apnea-ECG dataset from PhysioNet for sleep apnea detection and the Sleep-EDF dataset for REM detection. For sleep apnea, a ResNet-50 deep learning model is adapted to process ECG signals, treating them as image-like representations. ResNet-50 is trained on the Apnea-ECG dataset, which provides annotated electrocardiogram recordings for supervised learning. For REM detection, Gradient Boosting, an ensemble machine learning technique, is applied to EEG signals from the Sleep-EDF dataset. Relevant features associated with REM sleep phases are extracted from EEG signals and used to train the model. This paper contributes to automated sleep disorder diagnosis by presenting tailored machine learning models for detecting sleep apnea and REM from physiological signals.