A Machine Learning and Deep Learning Approach for Epileptic Seizure Detection

DOI

10.2991/978-94-6463-831-8_16How to use a DOI?

Keywords

EEG data; machine learning; deep learning; feature engineering; and epileptic seizure detection

Abstract

A chronic, non-communicable brain illness, epilepsy affects about 50 million people globally. Electroencephalography is one of the screening techniques that have been developed to detect epileptic episodes. EEG data provide vital information on the electrical activities of the brain and are often used to improve epilepsy analysis. Standard machine learning approaches were used to extract features prior to the development of deep learning (DL). They were therefore only as excellent as the individuals who manually created the features. However, DL completely automates both extraction and classification. The diagnosis of epilepsy is among the many medical specialties that have greatly benefited from these techniques. This study presents and compares the studies that have been done on automated epileptic seizure detection using machine learning and deep learning approaches. The training and validation dataset is the CHB-MIT Epileptic Seizure dataset. To determine the optimal strategy, a suggested model that makes use of long short-term memory (LSTM) is combined with some of the traditional ML and DL techniques. After then, different algorithms are compared to determine the most effective method for detecting epileptic seizures. In comparison to the other algorithms discussed in this study, the suggested model LSTM yields the most appropriate and accurate output with a validation accuracy of 97%.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Arti G. Ghule
AU  - Kalpana S. Thakre
PY  - 2025
DA  - 2025/08/31
TI  - A Machine Learning and Deep Learning Approach for Epileptic Seizure Detection
BT  - Proceeding of the 1st International Conference on Lifespan Innovation (ICLI 2025)
PB  - Atlantis Press
SP  - 126
EP  - 134
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6463-831-8_16
DO  - 10.2991/978-94-6463-831-8_16
ID  - Ghule2025
ER  -