A Novel Hybrid Attentive Convolutional Autoencoder (HACA) Framework for Enhanced Epileptic Seizure Detection

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.

@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},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Venkata Narayana Vaddi and Madhu Babu Sikha and Prakash Kodali},
  doi       = {10.14569/IJACSA.2025.01602127},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602127}
}