@inproceedings{f5354c22051c4d36a0788c0014834bf6,
title = "Modified ResNet50 with Transfer Learning for Enhanced Epileptic Activity Detection Using EEG Data",
abstract = "Epilepsy is a neurological disorder that significantly impacts individuals globally, particularly through recurrent seizures. Electroencephalograms (EEGs) are a crucial tool for diagnosing epilepsy, but manual analysis of EEG data is both labor-intensive and prone to errors. To address this, we propose an automated epilepsy detection system based on deep learning. Our approach utilizes a pre-trained ResNet50 model, specifically modified to accommodate the unique characteristics of EEG data through transfer learning. Evaluated on the CHB-MIT dataset, our model achieved an accuracy of 97\% on test data, demonstrating its ability to effectively generalize epilepsy-related patterns. This provides a reliable and efficient method for EEG-based seizure detection.",
keywords = "CHB-MIT Dataset, Deep Learning, Electroencephalogram (EEG), Epilepsy Detection, Transfer Learning",
author = "Zaef, \{Rieke Syochrani\} and Hendra Kusuma and Sardjono, \{Tri Arief\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2024 ; Conference date: 19-11-2024 Through 20-11-2024",
year = "2024",
doi = "10.1109/CENIM64038.2024.10882747",
language = "English",
series = "Proceedings of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2024",
address = "United States",
}