ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges

TL;DR

ProtoEEGNet is a novel interpretable deep learning model that detects interictal epileptiform discharges in EEG data with high accuracy and provides human-understandable justifications for its decisions, aiding medical diagnosis.

Contribution

It introduces ProtoEEGNet, a model combining state-of-the-art IED detection accuracy with interpretability through prototype-based reasoning, addressing the black-box issue in medical AI.

Findings

Achieves high accuracy in IED detection

Provides interpretable prototype-based explanations

Supports clinical validation of model decisions

Abstract

In electroencephalogram (EEG) recordings, the presence of interictal epileptiform discharges (IEDs) serves as a critical biomarker for seizures or seizure-like events.Detecting IEDs can be difficult; even highly trained experts disagree on the same sample. As a result, specialists have turned to machine-learning models for assistance. However, many existing models are black boxes and do not provide any human-interpretable reasoning for their decisions. In high-stakes medical applications, it is critical to have interpretable models so that experts can validate the reasoning of the model before making important diagnoses. We introduce ProtoEEGNet, a model that achieves state-of-the-art accuracy for IED detection while additionally providing an interpretable justification for its classifications. Specifically, it can reason that one EEG looks similar to another ''prototypical'' EEG that is known to contain an IED. ProtoEEGNet can therefore help medical professionals effectively detect IEDs while maintaining a transparent decision-making process.