Sleep Disorder Classification Using AI: A Machine Learning Approach

Abstract

To diagnose and treat sleep disorders, which have a high impact on the overall well-being, they have to be precisely defined. Traditional methods employ manual assessments, which are time and error-prone. A machine learning method of automatically classifying sleep disorders from EEG signals is proposed in this work. Utilizing MATLAB, the model is trained on EEG data to identify significant parameters such as frequency, amplitude, and wave patterns. Sleep disorders such as narcolepsy, sleep apnea, and insomnia are categorized based on machine learning models such as Support Vector Machine (SVM), Random Forest, and XGBoost. High accuracy and reliability are achieved by training and testing the model on publicly available EEG datasets. Performance analyses show enhanced rates of early detection and enhanced accuracy of classification. The method enhances automated diagnostic protocols, reducing the need for human evaluations and efficiency overall. Research in the future will continue to promote the use of deep learning methods to continue enhancing accuracy and adaptability in real world clinics.