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dc.contributor.author
Engemann, Denis A.
dc.contributor.author
Raimondo, Federico
dc.contributor.author
King, Jean Rémi
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Rohaut, Benjamin
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Louppe, Gilles
dc.contributor.author
Faugeras, Frédéric
dc.contributor.author
Annen, Jitka
dc.contributor.author
Cassol, Helena
dc.contributor.author
Gosseries, Olivia
dc.contributor.author
Fernandez Slezak, Diego
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Laureys, Steven
dc.contributor.author
Naccache, Lionel
dc.contributor.author
Dehaene, Stanislas
dc.contributor.author
Sitt, Jacobo Diego
dc.date.available
2020-01-17T21:56:25Z
dc.date.issued
2018-11
dc.identifier.citation
Engemann, Denis A.; Raimondo, Federico; King, Jean Rémi; Rohaut, Benjamin; Louppe, Gilles; et al.; Robust EEG-based cross-site and cross-protocol classification of states of consciousness; Oxford University Press; Brain; 141; 11; 11-2018; 3179-3192
dc.identifier.issn
0006-8950
dc.identifier.uri
http://hdl.handle.net/11336/95162
dc.description.abstract
Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ∼0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Oxford University Press
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BIOMARKER
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DIAGNOSIS
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DISORDERS OF CONSCIOUSNESS
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ELECTROENCEPHALOGRAPHY
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MACHINE LEARNING
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Otras Ciencias de la Computación e Información
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Robust EEG-based cross-site and cross-protocol classification of states of consciousness
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-12-16T19:15:13Z
dc.journal.volume
141
dc.journal.number
11
dc.journal.pagination
3179-3192
dc.journal.pais
Reino Unido
dc.journal.ciudad
Oxford
dc.description.fil
Fil: Engemann, Denis A.. Inserm; Francia
dc.description.fil
Fil: Raimondo, Federico. Inserm; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
dc.description.fil
Fil: King, Jean Rémi. Frankfurt Institute For Advanced Studies; Alemania. University of New York; Estados Unidos
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Fil: Rohaut, Benjamin. Inserm; Francia. Columbia University; Estados Unidos
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Fil: Louppe, Gilles. University of New York; Estados Unidos
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Fil: Faugeras, Frédéric. Inserm; Francia
dc.description.fil
Fil: Annen, Jitka. Centre Hospitalier Universitaire de Liege; Bélgica
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Fil: Cassol, Helena. Centre Hospitalier Universitaire de Liege; Bélgica
dc.description.fil
Fil: Gosseries, Olivia. Centre Hospitalier Universitaire de Liege; Bélgica
dc.description.fil
Fil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
dc.description.fil
Fil: Laureys, Steven. Centre Hospitalier Universitaire de Liege; Bélgica
dc.description.fil
Fil: Naccache, Lionel. Inserm; Francia
dc.description.fil
Fil: Dehaene, Stanislas. College de France; Francia
dc.description.fil
Fil: Sitt, Jacobo Diego. Inserm; Francia
dc.journal.title
Brain
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1093/brain/awy251
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/brain/article/141/11/3179/5114404
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