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<p>Distinguishing epileptic seizures from parasomnias is challenging due to overlapping motor features. This study evaluated a SlowFast deep learning model using video recordings of 167 individuals to classify Sleep-Related Hypermotor Epilepsy, Disorders of Arousal, and REM Sleep Behavior Disorder. The model achieved a mean accuracy of 83.3% across three data splits. This work represents an initial step toward developing automated tools to support clinicians in assessing sleep-related motor events.</p>

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Automated video-based differentiation of sleep-related hypermotor epilepsy and parasomnia episodes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • Matteo Moro,
  • Federica Sassi,
  • Ramona Cordani,
  • Anna Castelnovo,
  • Mauro Manconi,
  • Paola Proserpio,
  • Laura Tassi,
  • Federica Provini,
  • Francesca Odone,
  • Maura Casadio,
  • Lino Nobili,
  • Pietro Mattioli,
  • Dario Arnaldi,
  • Valentina Marazzotta,
  • Marco Veneruso,
  • Luca Baldelli,
  • Greta Mainieri,
  • Stefano Francione,
  • Luca Bosisio,
  • Alessandro Consales

摘要

Distinguishing epileptic seizures from parasomnias is challenging due to overlapping motor features. This study evaluated a SlowFast deep learning model using video recordings of 167 individuals to classify Sleep-Related Hypermotor Epilepsy, Disorders of Arousal, and REM Sleep Behavior Disorder. The model achieved a mean accuracy of 83.3% across three data splits. This work represents an initial step toward developing automated tools to support clinicians in assessing sleep-related motor events.