Individualized seizure cluster prediction using machine learning and ambulatory intracranial EEG

Seizure clusters, i.e., seizures that occur within a short duration of each other, occur in several epilepsy patients and are associated with increased disease severity. Understanding the characteristics of seizure clusters and predicting whether a given seizure will cluster or not is valuable both from a patient’s and clinician’s perspective. We propose a novel methodology for studying seizure clusters based on bivariate intracranial EEG (iEEG) features and develop one of the first individualized seizure cluster prediction models by combining machine learning with relative entropy (a bivariate feature). Relative entropy was used to quantify interactions between brain regions and capture potential differences in interactions underlying isolated and cluster seizures. We evaluated our methodology using one of the largest ambulatory iEEG datasets, consisting of data from 15 patients with up to 2 years of recordings each. This provided us a sufficient number of seizures in each patient to enable individualized analyses and prediction. On data of 3710 seizures consisting of 3341 cluster seizures (from 427 clusters) and 369 isolated seizures, machine learning models based on relative entropy predicted seizure clusters with up to 73.6% F1-score and outperformed baseline predictors. Our results are beneficial in addressing the clinical burden of clusters.