2019
DOI: 10.1097/ccm.0000000000003854
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Outcome Prediction in Postanoxic Coma With Deep Learning*

Abstract: Objectives: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent. Design: Prospective cohort study. Setting: Medical ICU of five teaching hospitals in the Netherlands. Patients: Eight-hundred ninety-five consecutive comatose patients after cardiac arrest. In… Show more

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Cited by 57 publications
(43 citation statements)
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“…Machine-learning algorithms could further facilitate outcome prediction using EEG, as has already been shown in the outcome prediction of comatose patients after cardiac arrest. 62…”
Section: Discussionmentioning
confidence: 99%
“…Machine-learning algorithms could further facilitate outcome prediction using EEG, as has already been shown in the outcome prediction of comatose patients after cardiac arrest. 62…”
Section: Discussionmentioning
confidence: 99%
“…This state known as "cognitive motor dissociation" was previously demonstrated in chronically unresponsive patients [80], and more recently in acute brain injury [78,81]. Similarly, the use of machine learning and quantitative analysis of longitudinal EEG signals shows promise in improving accuracy of commonly employed neurophysiologic predictors of outcome after cardiac arrest, such as background reactivity, epileptiform activity, and background continuity [79,82].…”
Section: Outcome Prediction In Disorders Of Consciousness Cognitionmentioning
confidence: 91%
“…A deep-learning artificial neural network analysing EEG data in comatose patients 12 h following cardiac arrest was able to predict 6-month functional outcome: good (48% accuracy and 0% falsepositive rate) versus bad (58% accuracy and 5% falsepositive rate). 17 Work in this area has also begun to go beyond passive recording and classification of inputs with the development of brain-machine interfaces that offer the intriguing potential to one day to communicate with people in a coma. 18…”
Section: Electrophysiology Interpretationmentioning
confidence: 99%
“…A deep-learning artificial neural network analysing EEG data in comatose patients 12 h following cardiac arrest was able to predict 6-month functional outcome: good (48% accuracy and 0% false-positive rate) versus bad (58% accuracy and 5% false-positive rate). 17…”
Section: Applications In Neurologymentioning
confidence: 99%