Seizure forecasting with multiple timescales and features

Abstract

Objective: Forecasting epileptic seizures is a difficult task. Studies of seizure prediction have investigated many different EEG features, but none of them have been useful enough to be applied in clinical practice beyond trials. Moreover, most of these features have been applied to short-term intracranial EEG (iEEG) recordings, limiting the possibility of reliable statistical evaluation. This paper proposes a machine learning algorithm to forecast an epileptic seizure 2-4 mins before seizure. This allows patients to seek help, or stimulation devices to work. Methods: This paper investigates a large subset of features from the past and present to unravel which features and feature analysis ..