  In intensive care units (ICUs), critically ill patients are monitored with
electroencephalograms (EEGs) to prevent serious brain injury. The number of
patients who can be monitored is constrained by the availability of trained
physicians to read EEGs, and EEG interpretation can be subjective and prone to
inter-observer variability. Automated deep learning systems for EEG could
reduce human bias and accelerate the diagnostic process. However, black box
deep learning models are untrustworthy, difficult to troubleshoot, and lack
accountability in real-world applications, leading to a lack of trust and
adoption by clinicians. To address these challenges, we propose a novel
interpretable deep learning model that not only predicts the presence of
harmful brainwave patterns but also provides high-quality case-based
explanations of its decisions. Our model performs better than the corresponding
black box model, despite being constrained to be interpretable. The learned 2D
embedded space provides the first global overview of the structure of
ictal-interictal-injury continuum brainwave patterns. The ability to understand
how our model arrived at its decisions will not only help clinicians to
diagnose and treat harmful brain activities more accurately but also increase
their trust and adoption of machine learning models in clinical practice; this
could be an integral component of the ICU neurologists' standard workflow.
