Ensemble learning framework for robust sleep stage classification using single-channel EEG

Refat, Fajle Rabbi and Jashim, Farhan Bin and Bhuiyan, Md Imranul Hoque and Masum, Abdullah Al and Pranta, Al Shahriar Uddin Khondakar (2025) Ensemble learning framework for robust sleep stage classification using single-channel EEG. International Journal of Science and Research Archive, 15 (2). pp. 1432-1441. ISSN 2582-8185

Sleep stage classification accuracy often suffers from inter-subject variability and signal artifacts. This study presents a novel ensemble learning framework for robust sleep stage classification using single-channel EEG data from the Physionet database. We develop specialized base classifiers optimized for each sleep stage transition and combine their outputs using a stacking approach with a meta-learner. Our framework employs confidence-weighted voting and a novel error-correction mechanism that identifies and rectifies physiologically implausible sleep stage transitions. Results demonstrate that the ensemble approach achieves 91.3% accuracy, outperforming individual classifier performance by 4.7-7.2%. Notably, the framework shows significantly improved robustness to artifacts, maintaining 89.6% accuracy when tested on noisy segments that cause individual classifiers to fail. The error-correction mechanism successfully identifies 93.4% of physiologically implausible transitions, improving temporal consistency. This methodology provides a powerful approach for reliable sleep staging in home environments where recording conditions may be suboptimal, offering potential for improved sleep disorder diagnosis outside laboratory settings.