[Research progress of epileptic seizure predictions based on electroencephalogram signals]

Abstract

As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.

Keywords: deep learning; electroencephalogram signals; epilepsy; machine learning; seizure prediction.

图 1

Four states of EEG in patients with epilepsy 癫痫患者脑电图的四个时期

图 2

Flow chart of seizure prediction 癫痫发作预测流程图

图 3

Schematic diagram of "k-of-n" module “k-of-n”模块示意图

图 4

Schematic diagram of the definition of SPH and SOP SPH和SOP的定义示意图