Automated identification of inter-ictal discharges using residual deep learning neural network amidst of various artefacts

Abstract

Visual analysis to identify inter-ictal activity in scalp EEG to support the diagnosis of epilepsy is a challenging task, which is embarked on by an experienced neurologist. Inter-Ictal state is a phase between convolutions (seizures) that are a feature of epilepsy disorder. The objective of this work is to automate the process of identification of inter-ictal activity and to distinguish it from the activity of a controlled patient with and without presence of artifacts