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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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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. |
format | Online Article Text |
id | pubmed-9924902 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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