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<p>Video-electroencephalography (vEEG) monitoring is currently the reference standard in the diagnosis of epilepsy. Manual analysis of vEEG recordings is time-consuming and inter-rater agreement is low even when the annotation is done by experienced doctors; therefore, there is a need for automated, standardized methods for vEEG annotation. Recent advances in machine learning have shown promise in real-time epileptiform discharge detection, as well as seizure detection and prediction using EEG and video data. However, the diversity of seizure symptoms, markup ambiguities, and the limited availability of multimodal datasets hinder progress. This paper reviews the latest developments in automated video-EEG analysis and discusses the integration of multimodal data, focusing on research published in 2024 and the beginning of 2025. We also propose a novel pipeline for explainable treatment effect estimation from vEEG data using concept-based learning, offering a pathway for future research in this domain.</p>

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Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges

  • 期刊论文

  • 发表日期：

    2025年10月20日

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  • Valerii A. Zuev,
  • Elena G. Salmagambetova,
  • Stepan N. Djakov,
  • Lev V. Utkin

摘要

Video-electroencephalography (vEEG) monitoring is currently the reference standard in the diagnosis of epilepsy. Manual analysis of vEEG recordings is time-consuming and inter-rater agreement is low even when the annotation is done by experienced doctors; therefore, there is a need for automated, standardized methods for vEEG annotation. Recent advances in machine learning have shown promise in real-time epileptiform discharge detection, as well as seizure detection and prediction using EEG and video data. However, the diversity of seizure symptoms, markup ambiguities, and the limited availability of multimodal datasets hinder progress. This paper reviews the latest developments in automated video-EEG analysis and discusses the integration of multimodal data, focusing on research published in 2024 and the beginning of 2025. We also propose a novel pipeline for explainable treatment effect estimation from vEEG data using concept-based learning, offering a pathway for future research in this domain.