Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast

Abstract

Objective: Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. Methods: This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracra..