Computational Approaches for Diagnosis and Monitoring of Epilepsy from Scalp EEG

Abstract

Epilepsy is a chronic brain disorder characterized by recurrent unprovoked seizures. It is caused by alterations in normal electrical activity in the brain, leading to various clinical manifestations depending on the regions that are affected. Scalp electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. It provides data that can pinpoint the foci of epileptiform disturbances and can characterize the epilepsy syndrome. However, providing a timely review of EEG data by clinical experts is a tedious and error-prone exercise. Moreover, there is a disparity in the global and national distribution of EEG experts. In order to assist EEG experts in reading EEGs, machine learning techniques can serve as valuable clinical tools to analyze EEG data in an objective and computationally efficient manner. Such methods have been developed mainly for two purposes in the context of epilepsy: for the detection of interictal epileptiform discharges (IED) and for the detection of electrographical epileptic seizures. Our aim is to concisely review state-of-the-art machine learning methods for IED and seizure detection, to elaborate on existing drawbacks and challenges for such approaches, and to provide guidance to physicians and researchers when designing an automated algorithm for the annotation of epileptic EEG. Furthermore, this chapter will outline potential future directions and opportunities for research in the diagnosis and monitoring of epilepsy from EEG recordings.