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.

翻译：在许多医学子领域中，临床工作中使用的机器学习系统要求具备更高的可解释性。本文设计了一个可解释的深度学习模型，用于预测重症监护病房（ICU）脑电图监测中常见的6种脑波模式（癫痫发作、LPD、GPD、LRDA、GRDA及其他）的存在性。每个预测都伴随着通过专用用户界面提供的高质量解释。这种新颖的模型架构学习一组原型示例（“原型”），并通过将新的脑电图片段与这些原型进行比较来做出决策。这些原型可以是单类别（仅与一个类别关联）或双类别（与两个类别关联）。我们提出了三种主要的模型解释方法：1）使用全局结构保留方法，将1275维的cEEG潜在特征映射到二维空间，以可视化癫痫发作-发作间期-损伤连续体，并洞察其高维结构；2）利用基于案例的推理进行预测，自然地提供“此脑电图与彼脑电图相似”形式的解释；3）将模型决策映射到二维空间，使用户能够看到当前样本预测与模型预测分布之间的比较。我们的模型在判别性性能指标AUROC（受试者工作特征曲线下面积）和AUPRC（精确率-召回率曲线下面积）以及任务特定可解释性指标上均优于对应的不可解释（黑箱）模型（p<0.01）。我们提供了展示二维嵌入空间探索的用户界面视频，首次呈现了癫痫发作-发作间期-损伤连续体脑波模式的全局结构概览。我们的可解释模型及专用用户界面可作为从事cEEG模式研究从业者的参考基准。