Sleep Stage Classification With Machine learning Models (RandomForestClassifier and DecisionTreeClassifier)

Abstract

Automated classification of sleep stages is in demand to overcome the limitations of manual sleep stage classification. Analyzing sleep stages manually using neurophysiological signals and inspecting visually is very difficult, time-consuming process. Many techniques have been proposed already in the past decades. Sleep experts, physicians do not have assurance with such techniques concerned with accuracy, specificity and sensitivity. Sleep state classification using electroencephalogram (EEG) signals is crucial for understanding sleep patterns and diagnosing sleep disorders. This thesis aims to improve the accuracy and robustness of sleep state classification by employing a voting technique that combines multiple classification models. The research involves preprocessing and feature extraction from EEG signals, training individual classification models, and applying a voting mechanism to make the final sleep state classification decision. The proposed approach aims to enhance the reliability of sleep stage classification and contribute to the field of sleep medicine. Statistical features are extracted and trained with Decision Tree, Support Vector Machine and Random Forest algorithms with different testing dataset percentage. Results show combination of Random forest and decision tree algorithm achieves 90% of accuracy.