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The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) recordings is a critical biomarker of epilepsy. Even trained neurologists find detecting IEDs difficult, leading many practitioners to turn towards machine learning for help. Although deep learning algorithms have shown state-of-the-art accuracy on this task, most models are uninterpretable and cannot justify their conclusions. Absent the ability to understand model reasoning, doctors cannot leverage their expertise to identify incorrect model predictions and intervene accordingly. To improve human-model interaction, we introduce ProtoEEG-kNN, an inherently interpretable IED-detection model that follows a simple case-based reasoning process. Specifically, ProtoEEG-kNN compares input EEGs to samples from the training set that contain similar IED morphology (shape) and spatial distribution (location). We show that ProtoEEG-kNN can achieve state-of-the-art accuracy while providing visual explanations that experts prefer over existing approaches.

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This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN

  • 会议论文

  • 发表日期：

    2026年

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  • Dennis Tang,
  • Jon Donnelly,
  • Alina Jade Barnett,
  • Lesia Semenova,
  • Jin Jing,
  • Peter Hadar,
  • Ioannis Karakis,
  • Olga Selioutski,
  • Kehan Zhao,
  • M. Brandon Westover,
  • Cynthia Rudin

摘要

The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) recordings is a critical biomarker of epilepsy. Even trained neurologists find detecting IEDs difficult, leading many practitioners to turn towards machine learning for help. Although deep learning algorithms have shown state-of-the-art accuracy on this task, most models are uninterpretable and cannot justify their conclusions. Absent the ability to understand model reasoning, doctors cannot leverage their expertise to identify incorrect model predictions and intervene accordingly. To improve human-model interaction, we introduce ProtoEEG-kNN, an inherently interpretable IED-detection model that follows a simple case-based reasoning process. Specifically, ProtoEEG-kNN compares input EEGs to samples from the training set that contain similar IED morphology (shape) and spatial distribution (location). We show that ProtoEEG-kNN can achieve state-of-the-art accuracy while providing visual explanations that experts prefer over existing approaches.