@article {688047,
	title = {Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning},
	journal = {IEEE Trans Biomed Eng},
	volume = {PP},
	year = {2021},
	month = {2021 Dec 28},
	abstract = {OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS: We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS: The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95\% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95\% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)\%, (55,95)\%, and (62,90)\%, (99,23)\%, (95,47)\%, and (90,62)\%; whereas for predicting good outcome, the corresponding operating points were (17,99)\%, (47,95)\%, (62,90)\%, (99,19)\%, (95,48)\%, (70,90)\%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE: These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.},
	issn = {1558-2531},
	doi = {10.1109/TBME.2021.3139007},
	author = {Zheng, Wei-Long and Amorim, Edilberto and Jin Jing and Wu, Ona and Mohammad Ghassemi and Lee, Jong Woo and Sivaraju, Adithya and Pang, Trudy and Herman, Susan T and Gaspard, Nicolas and Ruijter, Barry J and Tjepkema-Cloostermans, Marleen C and Hofmeijer, Jeannette and Michel Van Putten and Brandon Westover}
}