An Interpretable Machine Learning System to Identify EEG Patterns on the Ictal-Interictal-Injury Continuum

In many medical subfields, there is a call for greater interpretability in the machine learning systems used for clinical work. In this paper, we design an interpretable deep learning model to predict the presence of 6 types of brainwave patterns (Seizure, LPD, GPD, LRDA, GRDA, other) commonly encountered in ICU EEG monitoring. Each prediction is accompanied by a high-quality explanation delivered with the assistance of a specialized user interface. This novel model architecture learns a set of prototypical examples (``prototypes'') and makes decisions by comparing a new EEG segment to these prototypes. These prototypes are either single-class (affiliated with only one class) or dual-class (affiliated with two classes). We present three main ways of interpreting the model: 1) Using global-structure preserving methods, we map the 1275-dimensional cEEG latent features to a 2D space to visualize the ictal-interictal-injury continuum and gain insight into its high-dimensional structure. 2) Predictions are made using case-based reasoning, inherently providing explanations of the form ``this EEG looks like that EEG.'' 3) We map the model decisions to a 2D space, allowing a user to see how the current sample prediction compares to the distribution of predictions made by the model. Our model performs better than the corresponding uninterpretable (black box) model with $p<0.01$ for discriminatory performance metrics AUROC (area under the receiver operating characteristic curve) and AUPRC (area under the precision-recall curve), as well as for task-specific interpretability metrics. We provide videos of the user interface exploring the 2D embedded space, providing the first global overview of the structure of ictal-interictal-injury continuum brainwave patterns. Our interpretable model and specialized user interface can act as a reference for practitioners who work with cEEG patterns.