Epileptic seizure prediction using EEG peripheral channels

Abstract

Epilepsy is a neurological disease that causes uncontrollable seizures that can lead to severe or even lethal damage to the patient. This paper proposes an approach to predict epileptic seizures using peripheral electroencephalogram (EEG) channels from the CHB-MIT dataset. We created a machine learning algorithm to classify between interictal and preictal stages of seizures. The main goal is to assess the possibility of predicting these events using only peripheral channels and to present results for different configurations, such as the number of channels and their combinations. The preliminary performance of the algorithms is promising, with results similar to those in the literature that rely on channel reduction.