Investigation of fatigue mechanisms and detection methods for anesthesiologists based on multimodal physiological signals

Anesthesiologists are highly susceptible to fatigue due to the demanding intensity and critical responsibility of their work, which poses substantial risks to both clinician health and patient safety. To elucidate fatigue mechanisms, this study systematically assessed cognitive and physiological alterations before and after prolonged high-intensity work. Cognitive performance was evaluated with paradigms targeting attention (0-back), working memory (2-back), and visuospatial processing, complemented by multimodal physiological monitoring with electroencephalogram (EEG) and electrocardiogram (ECG) recordings. Prolonged work was associated with significant declines in n-back accuracy, reflecting impaired attention and working memory, while visuospatial performance showed marked increases in both error rate and reaction time, indicating deterioration of spatial cognition and executive control. Concurrently, physiological analyses revealed enhanced EEG alpha-band connectivity, shortened RR intervals, a reduced LF/HF ratio, and elevated multiscale entropy, collectively indicating autonomic imbalance and central–autonomic dysregulation under fatigue. Building on these mechanistic findings, we applied transfer learning algorithms to statistically significant multimodal physiological features, achieving 99.4% cross-subject classification accuracy. This integration of mechanistic insights with computational modeling underscores the reliability of the proposed strategy and its translational potential for real-world clinical fatigue monitoring. • Systematic evaluation of anesthesiologist fatigue using cognitive task paradigms • Multimodal EEG–ECG analysis reveals central–autonomic dysregulation under fatigue • Fatigue impairs attention, working memory, and visuospatial cognitive performance • Transfer learning on multimodal features achieves 99.4% cross-subject accuracy