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Auditory stimulation and deep learning predict awakening from coma after cardiac arrest

Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a ‘grey zone’, with unce...

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Autores principales: Aellen, Florence M, Alnes, Sigurd L, Loosli, Fabian, Rossetti, Andrea O, Zubler, Frédéric, De Lucia, Marzia, Tzovara, Athina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924902/
https://www.ncbi.nlm.nih.gov/pubmed/36637902
http://dx.doi.org/10.1093/brain/awac340
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author Aellen, Florence M
Alnes, Sigurd L
Loosli, Fabian
Rossetti, Andrea O
Zubler, Frédéric
De Lucia, Marzia
Tzovara, Athina
author_facet Aellen, Florence M
Alnes, Sigurd L
Loosli, Fabian
Rossetti, Andrea O
Zubler, Frédéric
De Lucia, Marzia
Tzovara, Athina
author_sort Aellen, Florence M
collection PubMed
description Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a ‘grey zone’, with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients’ chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients’ chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical ‘grey zone’. The network’s confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome.
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spelling pubmed-99249022023-02-14 Auditory stimulation and deep learning predict awakening from coma after cardiac arrest Aellen, Florence M Alnes, Sigurd L Loosli, Fabian Rossetti, Andrea O Zubler, Frédéric De Lucia, Marzia Tzovara, Athina Brain Original Article Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a ‘grey zone’, with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients’ chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients’ chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical ‘grey zone’. The network’s confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome. Oxford University Press 2023-01-13 /pmc/articles/PMC9924902/ /pubmed/36637902 http://dx.doi.org/10.1093/brain/awac340 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Aellen, Florence M
Alnes, Sigurd L
Loosli, Fabian
Rossetti, Andrea O
Zubler, Frédéric
De Lucia, Marzia
Tzovara, Athina
Auditory stimulation and deep learning predict awakening from coma after cardiac arrest
title Auditory stimulation and deep learning predict awakening from coma after cardiac arrest
title_full Auditory stimulation and deep learning predict awakening from coma after cardiac arrest
title_fullStr Auditory stimulation and deep learning predict awakening from coma after cardiac arrest
title_full_unstemmed Auditory stimulation and deep learning predict awakening from coma after cardiac arrest
title_short Auditory stimulation and deep learning predict awakening from coma after cardiac arrest
title_sort auditory stimulation and deep learning predict awakening from coma after cardiac arrest
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924902/
https://www.ncbi.nlm.nih.gov/pubmed/36637902
http://dx.doi.org/10.1093/brain/awac340
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