Implementation of convolution neural network using scalogram for identification of epileptic activity

Abstract

Inter-ictal state is a period between convolutions (seizures). Expert neurologist looks for inter-ictal activity within this period to support the diagnosis of epilepsy. The focus of this work is to automate the process of identification of inter-ictal activity from EEG and to distinguish it from the activity of a controlled patient. Also, we have worked on differentiating between different epileptic states. This work uses the Benchmark Bonn dataset and novel patient data collected from Max Hospital, Saket. Five groups are considered from Bonn database with the first four groups, with one case each and group five divided into ten cases and data collected from hospital is also considered. This study explores four cases under group 5, reporting of which is not available in the literature for bonn dataset and also reports the results obtained from data collected at Max Hospital. Two scenarios for Group 5 are presented under the first, the complete signal of length 23.6 s is converted into scalograms and in next scenario the complete signal is broken into segments of 2 s to make a comparative study with real time database.