Abstract
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 ...
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