Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals.

Journal: Revue neurologique
PMID: 40221359

Abstract

INTRODUCTION: Over the past decades, the integration of modern technologies - such as electronic health records, cloud computing, and artificial intelligence (AI) - has revolutionized the collection, storage, and analysis of medical data in neurology. In epilepsy, Interictal Epileptiform Discharges (IEDs) are the most established biomarker, indicating an increased likelihood of seizures. Their detection traditionally relies on visual EEG assessment, a time-consuming and subjective process contributing to a high misdiagnosis rate. These limitations have spurred the development of automated AI-driven approaches aimed at improving accuracy and efficiency in IED detection.

Authors

  • E Dessevres
    Laboratoire d'Imagerie Biomédicale (LIB) Inserm U1146, Sorbonne Université, UMR7371 CNRS, 15, Rue de l'École-de-Médecine, 75006 Paris, France.
  • M Valderrama
    Department of Biomedical Engineering, Universidad de Los Andes, 111711 Bogotá, Colombia.
  • M Le Van Quyen
    Laboratoire d'Imagerie Biomédicale (LIB) Inserm U1146, Sorbonne Université, UMR7371 CNRS, 15, Rue de l'École-de-Médecine, 75006 Paris, France. Electronic address: michel.le-van-quyen@inserm.fr.

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