Electroencephalography (EEG)-based epileptic seizure prediction using entropy and K-nearest neighbor (KNN)

Epilepsy is known as a brain disorder characterized by recurrent seizures. The development a system that able to predict seizure before its coming has several benefits such as allowing early treatment or even preventing the seizure. In this article, we propose a seizure prediction algorithm based on extracting Shannon entropy from electroencephalography (EEG) signals. K-nearest neighbor (KNN) method is used to continuously monitor the EEG signals by comparing with normal and pre-seizure baselines to predict the upcoming seizure. Both baselines are continuously updated based on the most recent prediction result using distance-based method. Our proposed algorithm is able to predict correctly 42 from 55 seizures (76 %), tested using up to 570 hours EEG taken from MIT dataset. With its simplicity and fast processing time, the proposed algorithm is suitable to be implemented in embedded system or mobile application that has limited processing resources