EEG-based epileptic seizure pattern decoding using vision transformer

Abdelhadi Hireche, Rafat Damseh, Parikshat Sirpal, Abdelkader Nasreddine Belkacem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Epilepsy is a prevalent neurological disorder and has been studied through the analysis of Electroencephalogram (EEG) signals. However, the identification and classification of epileptic seizure patterns remains challenging due to the non-stationary nature of EEG signals and the presence of artifacts. In this paper, we investigate the applicability of a transformer-based deep learning model to classify seizure patterns observed in epileptic patients. We employed the self-Attention mechanism inherent in transformers to capture complex temporal relationships in the EEG recordings. By prepossessing the EEG signals into suitable input sequences and adapting the transformer architecture, we achieved 78.11% in distinguishing between different epileptic seizure patterns. Our findings indicate that the transformer model, with its ability to manage long-range dependencies, offers a robust approach to EEG-based seizure pattern classification. This work is important for building advanced automated diagnostic tools for epilepsy and related neurological disorders.

Original languageEnglish
Title of host publication2023 15th International Conference on Innovations in Information Technology, IIT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages55-60
Number of pages6
ISBN (Electronic)9798350382396
DOIs
Publication statusPublished - 2023
Event15th International Conference on Innovations in Information Technology, IIT 2023 - Al Ain, United Arab Emirates
Duration: Nov 14 2023Nov 15 2023

Publication series

Name2023 15th International Conference on Innovations in Information Technology, IIT 2023

Conference

Conference15th International Conference on Innovations in Information Technology, IIT 2023
Country/TerritoryUnited Arab Emirates
CityAl Ain
Period11/14/2311/15/23

Keywords

  • Electroencephalogram
  • Epilepsy
  • Seizure
  • deep learning
  • vision transformer

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

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