A Novel Methodology to Detect Epileptic Seizure Based on EEG Signals Using Deep Learning Assisted Classification Principle

S. G. Balakrishnan, Kishore Kumar A., Naveenprasanth S., Mouleshwaran G. R., Naidu Raj Kumar

2025

Abstract

Analyzing the signals generated by neurons in the brain can reveal the presence of epilepsy, a severe and persistent neurological condition. A web of interconnected connections allows neurons to send and receive messages, as well as communicate with other parts of the body. The monitoring of these brain impulses is commonly done using electrocorticography (ECoG) and electroencephalography (EEG) equipment. These complex, noisy, non-linear, and non-stationary signals produce a mountain of data. As a result, detecting seizures and learning about brain-related topics is a challenging endeavor. Using Deep Learning classifiers, EEG data can be efficiently categorized, seizures can be identified, and relevant sensible patterns may be shown. As a result, several approaches to seizure detection have emerged, all utilizing Deep Learning classifiers and statistical data. The biggest challenge is choosing the right classifiers and attributes. The goal of this study is to present a comprehensive experimental review of the many different techniques that have arisen in the field of Deep Learning classifiers and statistical features in the past several years. In this paper, a new algorithm called Neural Classifier with Optimized Learning (NCOL) is presented. It can handle all the scenarios listed above and gives clear results. To test how well the algorithm works, it is cross-validated with the traditional Convolutional Neural Network (CNN) model. Seizure detection and categorization, as well as future research directions, may be better understood with the help of the offered state-of-the-art methodologies and concepts.

Paper Citation

in Harvard Style

Balakrishnan S., A. K., S. N., R. M. and Kumar N. (2025). A Novel Methodology to Detect Epileptic Seizure Based on EEG Signals Using Deep Learning Assisted Classification Principle. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 485-492. DOI: 10.5220/0013868000004919

in Bibtex Style

@conference{icrdicct`2525,
author={S. Balakrishnan and Kishore A. and Naveenprasanth S. and Mouleshwaran R. and Naidu Kumar},
title={A Novel Methodology to Detect Epileptic Seizure Based on EEG Signals Using Deep Learning Assisted Classification Principle},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={485-492},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013868000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - A Novel Methodology to Detect Epileptic Seizure Based on EEG Signals Using Deep Learning Assisted Classification Principle
SN - 978-989-758-777-1
AU - Balakrishnan S.
AU - A. K.
AU - S. N.
AU - R. M.
AU - Kumar N.
PY - 2025
SP - 485
EP - 492
DO - 10.5220/0013868000004919
PB - SciTePress