Epilepsy Diagnosis Using EEG Image Analysis

Seeba Doddmani, Sana Mulla, Dilipsingh Rajpurohit, Rajashri Khanai, Prema T. Akkasaligar

2025

Abstract

Epilepsy is a neurological disorder that affects millions of people around the world and is characterized by recurrent seizures caused by abnormal brain activity. Electroencephalograms (EEG) are the primary diagnostic tool, but traditional manual analysis is time-intensive and prone to errors. This project leverages machine learning techniques to automate epilepsy detection using scalogram images generated from EEG signals. A custom Convolutional Neural Network (CNN) model was developed and trained on the Bern-Barcelona EEG dataset, achieving a training precision of 74.07% and a testing precision of 73.22%. The model demonstrates good training performance and testing accuracy. An implemented VGG-16 gave a training accuracy of 81.13% and a testing accuracy of 80.04%. This study aims to help clinicians improve diagnostic accuracy and provide a scalable, real-time solution for epilepsy detection, particularly in underserved regions.

Paper Citation

in Harvard Style

Doddmani S., Mulla S., Rajpurohit D., Khanai R. and T. Akkasaligar P. (2025). Epilepsy Diagnosis Using EEG Image Analysis. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 776-783. DOI: 10.5220/0013602300004664

in Bibtex Style

@conference{incoft25,
author={Seeba Doddmani and Sana Mulla and Dilipsingh Rajpurohit and Rajashri Khanai and Prema T. Akkasaligar},
title={Epilepsy Diagnosis Using EEG Image Analysis},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={776-783},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013602300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Epilepsy Diagnosis Using EEG Image Analysis
SN - 978-989-758-763-4
AU - Doddmani S.
AU - Mulla S.
AU - Rajpurohit D.
AU - Khanai R.
AU - T. Akkasaligar P.
PY - 2025
SP - 776
EP - 783
DO - 10.5220/0013602300004664
PB - SciTePress