High density EEG and deep learning improves outcome prediction on the first day of coma after cardiac arrest

We assessed outcome prediction of comatose patients using a deep learning analysis applied to resting EEG on the first and second day after cardiac arrest (CA), and its added value to clinical prognosis. We recorded 62-channel resting-state EEG in comatose patients after CA across three Swiss hospitals during the first (N=165) and second (N=100) coma day. Patient outcome was classified as favorable if the best Cerebral Performance Category was 1-2. A convolutional neural network provided a predicted probability for favorable outcome for each patient’s and recording day’s 62-channel and 19-channel EEG. Predictive performance was additionally evaluated on an external 19-channel dataset collected outside Switzerland (N=60). The deep learning prediction was compared to EEG-based clinical markers - according to the American Clinical Neurophysiology Society -, brainstem reflexes and motor responses. On the first day, patient outcome was predicted with an accuracy of 0.94±0.03 for 62 channels and 0.90±0.03 and 0.87 for 19 channels using the Swiss and external dataset, respectively. High outcome prediction (0.98 accuracy) was observed when considering only patients with uncertain prognosis based on clinical assessment. The second day was less predictive, with an accuracy of 0.72±0.05. The estimated outcome prediction correlated with spectral power on the first day for favorable (r =0.38, p=0.01) and unfavorable (r=-0.28, p=0.02) outcome patients, and was consistent with clinical markers (p<0.0001), except brainstem reflexes. On the first day of coma in CA patients, a deep learning analysis of resting-state high-density EEG provides accurate outcome prediction, superior to lower-density EEG, and complements clinical markers.