Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings

José Yauri, Manuel Lagos, Hugo Vega-Huerta, Percy De-La-Cruz-VdV, Gisella Luisa Elena Maquen-Niño, Enrique Condor-Tinoco

Research output: Contribution to journalArticlepeer-review


According to the World Health Organization, epilepsy affects more than 50 million people in the world, and specifically, 80% of them live in developing countries. Therefore, epilepsy has become among the major public issue for many governments and deserves to be engaged. Epilepsy is characterized by uncontrollable seizures in the subject due to a sudden abnormal functionality of the brain. Recurrence of epilepsy attacks change people’s lives and interferes with their daily activities. Although epilepsy has no cure, it could be mitigated with an appropriated diagnosis and medication. Usually, epilepsy diagnosis is based on the analysis of an electroencephalogram (EEG) of the patient. However, the process of searching for seizure patterns in a multichannel EEG recording is a visual demanding and time consuming task, even for experienced neurologists. Despite the recent progress in automatic recognition of epilepsy, the multichannel nature of EEG recordings still challenges current methods. In this work, a new method to detect epilepsy in multichannel EEG recordings is proposed. First, the method uses convolutions to perform channel fusion, and next, a self-attention network extracts temporal features to classify between interictal and ictal epilepsy states. The method was validated in the public CHB-MIT dataset using the k-fold cross-validation and achieved 99.74% of specificity and 99.15% of sensitivity, surpassing current approaches.

Original languageEnglish
Pages (from-to)1067-1074
Number of pages8
JournalInternational Journal of Advanced Computer Science and Applications
Issue number5
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.


  • EEG
  • EEG channel fusion
  • Epilepsy
  • convolutional neural network
  • epilepsy detection
  • self-attention


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