SleepExplain: Explainable Non-Rapid Eye Movement and Rapid Eye Movement Sleep Stage Classification from EEG Signal
Researchers have developed a new model called SleepExplain for classifying sleep stages from EEG data. This model utilizes ensemble methods like XGBoost and Gradient Boosting, achieving high accuracy rates of up to 94.30%. To enhance transparency, SleepExplain incorporates SHAP (SHapley Addictive exPlanations) to provide clear justifications for its predictions, aiding in the diagnosis of sleep disorders. AI
IMPACT Enhances diagnostic capabilities for sleep disorders through explainable AI.