Preictal state detection using prodromal symptoms: A machine learning approach

A reliable identification of a high‐risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty‐four patients with drug‐resistant epilepsy were admitted for continuous video‐electroencephalographic monitoring and filled out a daily four‐point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24‐h period prior to at least one seizure ( n 1  = 58) and (2) an interictal group for questionnaires completed in a 24‐h period without seizures ( n 2  = 190). Our prediction model was based on a support vector machine classifier and compared to a Fisher's linear classifier. The combination of all the prodromal symptoms yielded a good prediction performance (area under the curve [AUC] = .72, 95% confidence interval [CI] = .61–.81). This performance was significantly enhanced by selecting a subset of the most relevant symptoms (AUC = .80, 95% CI = .69–.88). In comparison, the linear classifier systematically failed (AUCs < .6). Our findings indicate that the ML analysis of prodromal symptoms is a promising approach to identifying preictal states prior to seizures. This could pave the way for development of clinical strategies in seizure prevention and even a noninvasive alarm system.