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

TL;DR

ProtoEEG-kNN is an interpretable, case-based model for detecting interictal epileptiform discharges in EEGs, achieving high accuracy and providing visual explanations that enhance trust and understanding for clinicians.

Contribution

The paper introduces ProtoEEG-kNN, a novel interpretable model that combines accuracy with visual explanations for EEG IED detection, improving human-model interaction.

Findings

Achieves state-of-the-art accuracy in IED detection.

Provides visual explanations preferred by experts.

Balances interpretability with high performance.

Abstract

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 to machine learning for help. While existing machine learning algorithms can achieve strong 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 the human-model interaction, we introduce ProtoEEG-kNN, an inherently interpretable model that follows a simple case-based reasoning process. ProtoEEG-kNN reasons by comparing an EEG to similar EEGs from the training set and visually demonstrates its reasoning both in terms of IED morphology (shape) and spatial distribution (location). We show that ProtoEEG-kNN can achieve state-of-the-art accuracy in IED detection while providing explanations that experts prefer over existing approaches.