Sleep Disorder Detection Using EEG Signals

Abstract

Sleep disorders are prevalent health concerns affecting millions of individuals worldwide, with adverse impacts on overall well-being and cognitive function. Detecting and diagnosing these disorders accurately is crucial for effective treatment planning and management. This project focuses on utilizing Electroencephalogram (EEG) signals, a non-invasive method for monitoring brain activity, to detect sleeping disorders. By leveraging advanced signal processing techniques and machine learning algorithms, this research aims to develop a robust and accurate system capable of identifying various types of sleep disorders, such as insomnia, sleep apnea, and narcolepsy, based on EEG data. The proposed approach holds the potential to enhance early detection, personalized treatment strategies, and ultimately improve the quality of life for individuals affected by sleep disorders. Keywords-Ambulatory EEG, automatic scoring, deep learning, electroencephalography, sleep staging.