Abstract: Epilepsy, a prevalent neurological disorder, requires accurate and efficient seizure detection for timely intervention. This study presents a Hybrid Attentive Convolutional Autoen-coder (HACA) framework designed to address challenges in EEG signal processing for seizure detection. The proposed method integrates signal reconstruction, innovative feature extraction, and attention mechanisms to focus on seizure-critical patterns. Compared to conventional CNN- and RNN-based approaches, HACA demonstrates superior performance by enhancing feature representation and reducing redundant computations. The proposed HACA framework achieved 99.4% accuracy, 99.6%sensitivity, and 99.2% specificity on the CHB-MIT dataset. Moreover, the training time is reduced by 40%, which makes the model more relevant for real-time applications and portable seizure monitoring systems.

Venkata Narayana Vaddi, Madhu Babu Sikha and Prakash Kodali, “A Novel Hybrid Attentive Convolutional Autoencoder (HACA) Framework for Enhanced Epileptic Seizure Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602127

@article{Vaddi2025,
title = {A Novel Hybrid Attentive Convolutional Autoencoder (HACA) Framework for Enhanced Epileptic Seizure Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602127},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602127},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Venkata Narayana Vaddi and Madhu Babu Sikha and Prakash Kodali}
}