Leveraging Unlabeled Electroencephalographic Data to Predict Neurologic Recovery after Cardiac Arrest

Isaac Sears1, Augusta Garcia-Agundez2, George Zerveas2, William Rudman2, Laura Mercurio3, Corey Ventetuolo4, Adeel Abbasi4, Carsten Eickhoff5
1Warren Alpert Medical School, 2Department of Computer Science, Brown University, 3Department of Emergency Medicine, Section of Pediatric Emergency Medicine, Warren Alpert Medical School at Brown University, 4Department of Medicine, Warren Alpert Medical School at Brown University, 5Institute for Bioinformatics and Medical Informatics, University of Tübingen

Abstract

Rationale: Neurologic prognostication following cardiac arrest remains challenging. Electroencephalography (EEG) can aid with real-time prognostication but the sheer mass of data generated requires an automated analytic approach. In response to the 2023 George B. Moody PhysioNet Challenge, we propose an automated, unsupervised pre-training approach to predict neurologic outcomes after cardiac arrest.

Methods: Our model architecture consisted of three parts: a pre-processor to convert raw EEGs to two-dimensional spectrograms, a three-layer convolutional autoencoder (CAE) for unsupervised pre-training, and a Time Series Transformer (TST). We trained the CAE on randomly selected five-minute EEG samples from the Temple University EEG Corpus (TUEG). We then incorporated the pre-trained encoder into the TST as a base layer and trained the model as a classifier on EEGs from the 2023 PhysioNet Challenge dataset. Model performance was assessed using F1-score with five-fold cross validation. For comparison, a TST with a convolutional base layer that had been randomly initialized, rather than pre-trained, was tested.

Results: The TUEG dataset included 14,927 subjects. The 2023 PhysioNet Challenge dataset included 607 post-cardiac arrest patients with a mean of 189 minutes of EEG data per patient. Neurologic outcomes were classified as "poor” (37.1%) or "good” (62.9%). In a side-by-side comparison, our model performed better when the CAE layer, before being trained in tandem with the TST weights, was pre-trained on unlabeled EEG data, F1-score 0.762 ± 0.017, rather than randomly initialized (0.683 ± 0.032).

Conclusion: The CAE was able to learn latent representations of EEGs by training on a large unlabeled dataset not specifically curated for the task of predicting post-arrest outcomes. These latent representations proved useful for training the TST to predict post-arrest outcomes from the labeled PhysioNet dataset, demonstrating that unlabeled EEG data can be leveraged to improve performance on other modeling tasks that depend on smaller labeled EEG datasets.