Evaluating the Fitness-to-Drive Using Evoked Visual Responses in Alzheimer's Disease

Alzheimer's Disease (AD) is the sixth leading cause of death in the US. AD causes significant disability due to the devastating impact on the patients' day-to-day living activities and their loss of independence. One such day-to-day activity is driving, a complex task that requires attention, concentration, the ability to follow particular steps, react to stimuli promptly, and the ability to perceive and interpret visual-spatial information, all of which can be impaired in AD. Therefore, to ensure the safety of AD patients and other drivers, it is important to develop accurate and low-cost diagnostic tools to assess patients' fitness-to-drive. In this study, we develop machine learning (ML) models to predict fitness-to-drive using the electroencephalogram (EEG) technique of event-related potential (ERP). Specifically, we develop random forest (RF) models using EEG signals in early-stage AD patients and age-matched controls and conduct numerical experiments to predict fitness-to-drive and other driving performance metrics, collected from driving simulator data. Our results show that RF models predict patients' fitness-to-drive with AUC=0.83 and provide accurate measures of other driving performance metrics. Therefore, ML and ERP offer a valuable approach to assess driving safety for patients with early AD symptoms in the laboratory setting.