PSG Sleep Stage Classification using Data Mining

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This presentation explores the pivotal role of sleep stage classification in modern sleep research, emphasizing the challenges of manual sleep scoring and how automation enhances scalability and efficiency. We delve into the dataset comprising patient EEG, EOG, and EMG signals, followed by insights on feature engineering using PCA for effective feature separation. A Random Forest approach demonstrates improved detection accuracy, particularly for minority stages. Highlighted key features and...