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Automated identification and localization of interictal epileptiform discharges: leveraging morphological analysis, five-criterion fulfillment, and machine learning approach

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Abstract

Interictal epileptiform discharges (IEDs) play a crucial role in the diagnosis and assessment of seizure risk in epilepsy. Their frequency, amplitude, and morphological characteristics serve as consistent markers of epileptogenesis. Currently, the clinical evaluation of IEDs heavily relies on visual detection by specialized experts, which is a subjective and time-consuming task. To address this, there is a need for an automated IEDs detection system that can provide faster and more reliable epilepsy diagnosis. In this paper, we propose a novel method for automatic identification of IEDs by inspecting the fulfillment of specific criteria. The decisions made by our method were compared with the combined decisions of three neurologists, serving as a benchmark for evaluation. The results and performance metrics obtained from this comparison demonstrate the effectiveness and potential of our automated IEDs identification method.

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Data availability

The EEG data are available online. The interictal epileptiform discharges annotations are kept confidential.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Research Groups Program under grant number (RGP2/166/44). Authors would like to thank the clinical partners for their cooperation.

Funding

King Khalid University, RGP2/166/44.

Author information

Authors and Affiliations

  1. Advanced Technologies for Medicine and Signals ‘ATMS’, ENIS, Sfax University, Sfax, Tunisia

    Omar Trigui & Mohamed Ghorbel

  2. Department of Neurology, Habib Bourguiba University Hospital, Sfax, Tunisia

    Sawsan Daoud, Mariem Dammak & Chokri Mhiri

  3. Neuroscience Laboratory LR-12-SP-19, Sfax University, Sfax, Tunisia

    Sawsan Daoud, Mariem Dammak & Chokri Mhiri

  4. Department IS, College of Computer Science, King Khaled Universiy ‘KKU’, Abha, Saudi Arabia

    Ahmed Ben Hamida

Authors
  1. Omar Trigui
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  2. Sawsan Daoud
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  4. Mariem Dammak
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  5. Chokri Mhiri
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Contributions

—O. Trigui and S. Dewid contributed by the design of the processing method, examination of results, and manuscript writing. —S. Dewid, M. Dammak, and C. Mhiri contributed by the annotation of the database. —M. Ghorbel, M. Dammak, C. Mhiri, and A. Ben Hamida contributed equally by the supervision of the work.

Corresponding author

Correspondence to Omar Trigui.

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Conflict of interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ahmed Ben Hamida reports that financial support was provided by King Khaled Universiy.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Trigui, O., Daoud, S., Ghorbel, M. et al. Automated identification and localization of interictal epileptiform discharges: leveraging morphological analysis, five-criterion fulfillment, and machine learning approach. J Supercomput 81, 1 (2025). https://doi.org/10.1007/s11227-024-06558-z

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  • DOI: https://doi.org/10.1007/s11227-024-06558-z

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