Epileptic Multi-seizure Type Classification Using Electroencephalogram Signals from the Temple University Hospital Seizure Corpus: A Review

Niamh McCallan, Scot Davidson, Kok Yew Ng, Pardis Biglarbeigi, D Finlay, Boon Leong Lan, James McLaughlin

Research output: Contribution to journalReview articlepeer-review

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Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the world's population. Seizure detection and classification are difficult tasks and are ongoing challenges in biomedical signal processing to enhance medical diagnosis. This paper presents and highlights the unique frequency and amplitude information found within multiple seizure types, including their morphologies, to aid the development of future seizure classification algorithms. Whilst many published works in the literature have reported on seizure detection using electroencephalogram (EEG), there has yet to be an exhaustive review detailing multi-seizure type classification using EEG. Therefore, this paper also includes a detailed review of multi-seizure type classification performance based on the Temple University Hospital Seizure Corpus (TUSZ) dataset for focal and generalised classification, and multi-seizure type classification. Deep learning techniques have a higher overall average performance for focal and generalised classification compared to machine learning techniques, whereas hybrid deep learning approaches have the highest overall average performance for multi-seizure type classification. Finally, this paper also highlights the limitations of the TUSZ dataset and suggests some future work, including the curation of a standardised training and testing dataset from the TUSZ that would allow a proper comparison of classification methods and spur advancement in the field.

Original languageEnglish
Article number121040
JournalExpert Systems with Applications
Early online date27 Jul 2023
Publication statusPublished online - 27 Jul 2023

Bibliographical note

Funding Information:
Miss Niamh McCallan’s PhD work is funded by the Department for the Economy (DFE) awarded by the Ulster University, UK .

Publisher Copyright:
© 2023 The Authors


  • Biomedical signal processing
  • Deep learning
  • Electroencephalogram (EEG)
  • Feature extraction
  • Machine learning
  • Multi-seizure type classification


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