Machine Learning Based Assessment of Cognitive Performance Under Sleep Deprivation

This study investigates the integration of multiple biological signals to assess the impact of sleep deprivation on attention levels. Electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG) data from sleep-deprived patients were analyzed with performance outcomes from the Psychomotor Vigilance Test (PVT), which measures response times. The primary objective was to develop a robust predictive model for the level of drowsiness based on these signals. By leveraging machine learning models, the study demonstrated the feasibility of signal-based assessments for predicting drowsiness levels. Random Forest achieved the highest accuracy when using reaction times as the true labels. It also showed promising agreement with the subjective evaluation of the alertness levels, highlighting conditions where the individuals may risk to underestimate their drowsiness. The results underscore the potential of biological signals to improve understanding of sleep deprivation's impact on cognitive performance and potentially contribute to develop robust drowsiness detection systems for practical contexts.