ProtoEEGNet: An Interpretable Approach for Detecting Interictal
  Epileptiform Discharges
Dennis Tang Frank Willard Ronan Tegerdine Luke Triplett Jon Donnelly Luke Moffett Lesia Semenova Alina Jade Barnett Jin Jing Cynthia Rudin Brandon Westover
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.