SleepExplain: Explainable Non-Rapid Eye Movement and Rapid Eye Movement Sleep Stage Classification from EEG Signal

Abstract:Classification of sleep stages is one of the most important diagnostic approaches for a variety of sleep-related disorders. Electroencephalography (EEG) is regarded as a powerful tool for examining the association between neurological effects and sleep phases since it correctly identifies sleep-related neurological alterations. During Non-Rapid Eye Movement (NREM) and Rapid Eye Movement (REM) sleep phases, a number of nerve and bodily functions are affected and therefore hold an important role both in their functionalities. This work aims to classify NREM and REM sleep stages from sleep EEG data and present a noble SleepExplain model, an explainable NREM and REM sleep stage classification to explain its predictions. In this work, sleep stages were classified using Random Forest, XGBoost, and Gradient Boosting ensemble classification models. Overall, we obtained an accuracy of 92.54% (Random Forest), 94.25% (Gradient Boosting), and 94.30% (XGBoost). For explainable classification model, we utilized a game theoretic approach, SHAP (SHapley Addictive exPlanations) to offer a convincing explanation for the prediction.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.07351 [cs.LG]

(or arXiv:2606.07351v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.07351 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2022 25th International Conference on Computer and Information Technology (ICCIT), pp. 248-253, 2022

https://doi.org/10.1109/ICCIT57492.2022.10055956 Focus to learn more DOI(s) linking to related resources

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