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Accurate seizure detection is vital for effective epilepsy management, often relying on multimodal data, such as video, EEG, and ECG, to capture comprehensive diagnostic information. However, integrating diverse modalities poses challenges, including handling missing data, aligning disparate formats, and achieving seamless fusion. This study focuses on utilizing non-invasive, privacy-preserving modalities, optical flow and pose (body, face, hand) extracted from video, alongside ECG recordings, to differentiate between Generalized Tonic-Clonic Seizures (GTCS) and Psychogenic NonEpileptic Seizures (PNES). To address these challenges, we propose two novel approaches: the Pose Attention Graph (PAG), a symmetric graph model for analyzing patient movement, and the Modalities Relational Graph (MRG) for dynamic coordination of modality interactions. Evaluated on our in-house multimodal (ECG+Video) dataset, the model achieves an 80.64for seizure detection using 10-second snippets, 80.11Furthermore, it maintains robust performance with a precision of 77.72even with missing modalities, addressing key challenges in multimodal seizure detection. Our code is available at: GitHub .

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Privacy-Centric Seizure Diagnosis via Relation-Aware Fusion of Minimally-Invasive Modalities

  • 会议论文

  • 发表日期：

    2026年

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  • Talha Ilyas,
  • Deval Mehta,
  • Shobi Sivathamboo,
  • Ilma Wijaya,
  • Rob Steele,
  • Hugh Simpson,
  • Lyn Millist,
  • Terence O’Brien,
  • Patrick Kwan,
  • Zongyuan Ge

摘要

Accurate seizure detection is vital for effective epilepsy management, often relying on multimodal data, such as video, EEG, and ECG, to capture comprehensive diagnostic information. However, integrating diverse modalities poses challenges, including handling missing data, aligning disparate formats, and achieving seamless fusion. This study focuses on utilizing non-invasive, privacy-preserving modalities, optical flow and pose (body, face, hand) extracted from video, alongside ECG recordings, to differentiate between Generalized Tonic-Clonic Seizures (GTCS) and Psychogenic NonEpileptic Seizures (PNES). To address these challenges, we propose two novel approaches: the Pose Attention Graph (PAG), a symmetric graph model for analyzing patient movement, and the Modalities Relational Graph (MRG) for dynamic coordination of modality interactions. Evaluated on our in-house multimodal (ECG+Video) dataset, the model achieves an 80.64for seizure detection using 10-second snippets, 80.11Furthermore, it maintains robust performance with a precision of 77.72even with missing modalities, addressing key challenges in multimodal seizure detection. Our code is available at: GitHub .