用可解释模型准确区分睡眠阶段，提升医学诊断可信度。
SleepExplain: Explainable Non-Rapid Eye Movement and Rapid Eye Movement Sleep Stage Classification from EEG Signal
- 基于随机森林、XGBoost等集成模型分类睡眠阶段
- 最高准确率达94.30%，显著优于传统方法
- 引入SHAP解释模型决策，适合临床医生理解
睡眠分期是多种睡眠障碍诊断的重要手段。脑电图（EEG）能有效捕捉神经活动与睡眠阶段的关联，准确识别与睡眠相关的神经变化。在非快速眼动（NREM）和快速眼动（REM）睡眠阶段，多种神经与生理功能发生变化，具有重要临床意义。本文提出一种名为SleepExplain的可解释分类模型，用于从睡眠EEG数据中区分NREM与REM睡眠阶段。采用随机森林、XGBoost和梯度提升集成模型进行分类，分别获得92.54%（随机森林）、94.25%（梯度提升）和94.30%（XGBoost）的准确率。为增强模型可解释性，引入博弈论框架下的SHAP（SHapley Additive exPlanations）方法，提供预测结果的可信解释，助力临床判读。
原文摘要 · Abstract (English)
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.
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