Phasing of epileptic seizure spreading and extending using hidden Markov model

Abstract

This paper argues the phasing of epileptic seizure spreading and extending using the hidden Markov model (HMM). The intracranial electroencephalography during an epileptic seizure is thought to have several phases with time transitions. In this paper, we identified clinically interpretable phases in all 30 cases of epileptic seizures by unsupervised learning of HMM. Data-driven discovery of phases may contribute to understanding the mechanisms of epileptic seizure onset to settle and lead to new treatments.