Identification of Inter-ictal Activity from EEG Signal Using Scalograms with LeNet-5 Based Model

Abstract

Identification of inter-ictal activity has always presented as a diagnostic challenge, for neurologist consuming much of their time. The automation of the process can provide the required support to the neurologist. Publically available Bonn data dataset has been used for this work. We have created two second segments of public data and created its scalogram which acts as an input to our model, whereas earlier researchers have worked on complete 23.6 s data. LeNet-5-based model is used as classifier. The goal of this work is to distinguish inter-ictal activity with and without presence of various artifacts. Accuracy of 98.03% has been accomplished for the public dataset.