THE ROLE OF MACHINE LEARNING IN ENHANCING SLEEP STAGE DETECTION ACCURACY WITH SINGLE-CHANNEL EEG
Keywords:
Sleep Stage Detection, Machine Learning, Single-channel EEG, Convolutional Neural Networks (CNN), Random Forest, Support Vector Machines (SVM), Deep Learning, Sleep MonitoringAbstract
Accurate sleep stage detection is essential for diagnosing sleep disorders and understanding sleep patterns. Traditional methods, such as manual scoring of polysomnographic data and the use of multi-channel EEG, while effective, are resource-intensive and impractical for home-based monitoring. This study explores the application of machine learning models, specifically deep learning techniques like Convolutional Neural Networks (CNNs), to improve the accuracy of sleep stage detection using single-channel EEG data. By comparing the performance of CNNs, Random Forest, and Support Vector Machines (SVM), the study demonstrates that CNNs significantly outperform other models in terms of accuracy, sensitivity, and specificity, achieving an accuracy of 91.4%. The results suggest that machine learning, particularly CNNs, offers a practical solution for reducing the complexity of sleep monitoring while maintaining high accuracy, making it viable for both clinical and non-clinical settings, including wearable and home-based devices. These findings highlight the potential of machine learning in transforming sleep stage detection into a more accessible and user-friendly process.
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Copyright (c) -1 Souptik Sen, Ramesh Krishnamaneni, Ashwin Narasimha Murthy (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
