Machine Learning Approach to Sleep Stage Classification Based on EEG Signal

This study focuses on the integration of biomathematical models, machine learning, and wearable sensor technologies for better understanding and predicting sleep quality. A detailed literature review showed that such technologies could bring about a revolutionary change while raising issues like huge data requirements and personalization. Using the Sleep Health and Lifestyle Dataset, machine learning models were tested, which includes K-Nearest Neighbors (KNN) and XGBoost with a 0.97 accuracy, Decision Tree and LightGBM at 0.96, SVM and Logistic Regression with 0.90 accuracy, which proved to be strong in terms of versatility and interpretability. These models exhibited high precision and recall, thus enabling remarkable progress in the identification and treatment of sleep-related problems. Future work includes expansion of datasets, feature engineering, and the study of more advanced algorithms like deep learning. Applications like personal sleep improvement tools and early detection systems for sleep disorders hold much promise for translating these findings into impactful health solutions.