Abstract
Objective
Electroencephalography (EEG) is an important tool for neurological outcome prediction
after cardiac arrest. However, the complexity of continuous EEG data limits timely
and accurate interpretation by clinicians. We develop a deep neural network (DNN)
model to leverage complex EEG trends for early and accurate assessment of cardiac
arrest coma recovery likelihood.
Methods
We developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent
neural networks (long short-term memory [LSTM]) using EEG and demographic information
(age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest
patients. The CNN learns EEG feature representations while the multiscale LSTM captures
short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined
as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score
1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under
the receiver operating characteristic curve (AUC) and calibration error.
Results
Model performance increased with EEG duration, with AUC increasing from 0.83 (95%
Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity
of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively.
Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability
was well matched to the observation frequency of poor outcomes, with a calibration
error of 0.11 [0.09-0.14].
Conclusions
These results demonstrate that incorporating EEG evolution over time improves the
accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.
Keywords
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Article info
Publication history
Published online: October 23, 2021
Accepted:
October 20,
2021
Received in revised form:
October 19,
2021
Received:
August 31,
2021
Identification
Copyright
© 2021 Elsevier B.V. All rights reserved.
