Tracking Vigilance While Driving: Pilot Study of Heart Rate Variability Classification Under a Controlled Sleep-Deprivation Protocol.

Decreased vigilance associated with drowsiness is a major cause of motor vehicle crashes, but its physiological characterization and detection remain difficult due to behavioral and environmental confounding factors. This study presents an original experimental framework combining a highly controlled sleep-deprivation protocol, repeated driving simulations, and a machine-learning-based heart rate variability (HRV) analysis to study the physiological and behavioral changes in vigilance during a prolonged period of wakefulness while driving. Twelve healthy young adults participated in a 30-h sleep-deprivation constant routine protocol that included driving simulations every two hours, with data collection: electrocardiography (ECG), subjective ratings of drowsiness, psychomotor vigilance test, and driving performance. The collected data was separated into two clearly identifiable vigilance states, a "Rested" and a "Tired" state, respectively, for the first and last driving sessions, of the protocol. Heart rate variability (HRV) features were extracted from ECG recordings, statistically analyzed, and submitted into supervised machine learning classifiers using participant-independent validation to assess their ability to distinguish between two experimentally defined states of vigilance. The accuracy of state separation was verified based on significant alterations observed in drowsiness ratings and psychomotor performance in the Tired compared to the Rested state. The variability in driving behavior, an indicator of impaired driving ability, increased significantly from the Rested compared to the Tired state. Several HRV features revealed significant differences between Rested and Tired states, with effect sizes ranging from moderate to large, observed for several metrics. Participant-independent machine learning analysis, namely Leave-One-Subject-Out Cross-Validation (LOSO-CV), showed that HRV metrics enable reliable discrimination between vigilance states, with all classifiers evaluated achieving average accuracy greater than 85%. This pilot study demonstrated that the proposed experimental framework, by reducing the major behavioral and environmental confounding factors, can generate two distinct states of vigilance at the physiological and behavioral levels, enabling reliable separation of experimental data, independent of any additional ground truths. The states separated by the framework enable the detection of decreased driving performance and efficiently label HRV data for vigilance classification. Nevertheless, the findings should be interpreted within the context of a pilot study conducted with a small cohort of healthy young adults.