A continuous convolutional neural network: very early EEG most predictive for poor neurological outcome in postanoxic coma

The CNN seems valuable for poor neurological outcome prediction in postanoxic coma patients, especially for very early (6-14 h after CA) EEG. A reduced electrode configuration is equally sufficient as the full set. Future studies could explore capturing nuanced temporal dependencies in EEG signals for prognostication.

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Publication Details

Journal
Resuscitation
Published
2025-12-25
DOI
https://doi.org/10.1016/j.resuscitation.2025.110943
Citations
1
Primary Topic
Cardiac Arrest and Resuscitation
Type
article
Field-Weighted Citation Impact
2.25
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article

A continuous convolutional neural network: very early EEG most predictive for poor neurological outcome in postanoxic coma

Brian S. Doelkahar, Janneke Horn, Inayah Hodžić, Anne-Fleur van Rootselaar
1 citations
Resuscitation
Cardiac Arrest and Resuscitation
2.25
article

A continuous convolutional neural network: very early EEG most predictive for poor neurological outcome in postanoxic coma

Brian S. Doelkahar, Janneke Horn, Inayah Hodžić, Anne-Fleur van Rootselaar
article en
1 citations

Abstract

The CNN seems valuable for poor neurological outcome prediction in postanoxic coma patients, especially for very early (6-14 h after CA) EEG. A reduced electrode configuration is equally sufficient as the full set. Future studies could explore capturing nuanced temporal dependencies in EEG signals for prognostication.

ResuscitationVol. 219
Amsterdam Neuroscience (NL), Amsterdam University Medical Centers (NL), University of Amsterdam (NL)
No poverty
Openalex Percentile: Top 9%
Cardiac Arrest and Resuscitation
2.25
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