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

Arshpreet Kaur and Kumar Shashvat

Chaos, Solitons & Fractals, 2022, vol. 162, issue C

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.

Keywords: Continuous wavelet transform; Convolution neural network; EEG; Epilepsy; Classification (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077922007275
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:162:y:2022:i:c:s0960077922007275

DOI: 10.1016/j.chaos.2022.112528

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().