Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection

Background: Fatigue detection through physiological signals has gained growing relevance across safety-critical domains such as transportation, healthcare, and human performance monitoring. While many studies focus on individual modalities (e.g., EEG or ECG), limited attention has been given to investigating statistical relationships between signal pairs as a means to enhance classification robustness. This study aims to explore how inter-signal statistical features correlation, cross-correlation, and covariance across multiple physiological signals can support fatigue state prediction. Methodology: Using the DROZY dataset, we extracted pairwise statistical features from four physiological signals: ECG, EMG, EOG, and EEG. Fifteen distinct signal combinations were evaluated, covering uni-modal to multi-modal configurations. Feature extraction emphasized statistical relationships between signals rather than raw amplitude characteristics. The extracted features were fed into four supervised machine learning classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and XGBoost (XGB). Performance was assessed using accuracy, precision, recall, and area under the curve (AUC). Additionally, SHAP (SHapley Additive exPlanations) values were computed to evaluate feature importance and interpret model behavior. Results: Among all classifiers and signal combinations, XGBoost applied to the EMG| EEG combination achieved the highest classification performance, with an accuracy of 0.888 and an AUC of 0.975. SHAP-based ranking revealed that the correlation between ECG and EOG-H was the most influential feature across models. Feature interaction plots indicated non-linear relationships between statistical measures and fatigue levels. The multi-signal approach consistently outperformed single-signal models, with combinations involving EEG and EMG contributing most significantly to predictive power.