Toward an Attention-Based Diagnostic Tool for Patients With Locked-in Syndrome

[en] Electroencephalography (EEG) has been proposed as a supplemental tool for reducing clinical misdiagnosis in severely brain-injured populations helping to distinguish conscious from unconscious patients. We studied the use of spectral entropy as a measure of focal attention in order to develop a motor-independent, portable, and objective diagnostic tool for patients with locked-in syndrome (LIS), answering the issues of accuracy and training requirement. Data from 20 healthy volunteers, 6 LIS patients, and 10 patients with a vegetative state/unresponsive wakefulness syndrome (VS/UWS) were included. Spectral entropy was computed during a gaze-independent 2-class (attention vs rest) paradigm, and compared with EEG rhythms (delta, theta, alpha, and beta) classification. Spectral entropy classification during the attention-rest paradigm showed 93% and 91% accuracy in healthy volunteers and LIS patients respectively. VS/UWS patients were at chance level. EEG rhythms classification reached a lower accuracy than spectral entropy. Resting-state EEG spectral entropy could not distinguish individual VS/UWS patients from LIS patients. The present study provides evidence that an EEG-based measure of attention could detect command-following in patients with severe motor disabilities. The entropy system could detect a response to command in all healthy subjects and LIS patients, while none of the VS/UWS patients showed a response to command using this system. © 2016, © EEG and Clinical Neuroscience Society (ECNS) 2016.

Disciplines :

Neurologie

Auteur, co-auteur :

Lesenfants, Damien ; Coma Science Group, GIGA-Research, CHU University Hospital of Liege, Liege, Belgium, School of Engineering and Institute for Brain Science, Brown University, Providence, RI, United States, Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of VA Medical Center, Providence, RI, United States

Habbal, Dina ; Coma Science Group, GIGA-Research, CHU University Hospital of Liege, Liege, Belgium

Chatelle, Camille ; Université de Liège - ULiège > GIGA : Coma Group

Soddu, Andrea ; Université de Liège - ULiège > Centre de recherches du cyclotron

Laureys, Steven ; Université de Liège - ULiège > GIGA : Coma Group

Noirhomme, Quentin ; Université de Liège - ULiège > Centre de recherches du cyclotron

Langue du document :

Anglais

Titre :

Toward an Attention-Based Diagnostic Tool for Patients With Locked-in Syndrome

Date de publication/diffusion :

2018

Titre du périodique :

Clinical EEG and Neuroscience

ISSN :

1550-0594

eISSN :

2169-5202

Maison d'édition :

SAGE Publications Inc.

Volume/Tome :

Fascicule/Saison :

Pagination :

122-135

Peer reviewed :

Peer reviewed vérifié par ORBi

Projet européen :

FP7 - 247919 - DECODER - Deployment of Brain-Computer Interfaces for the Detection of Consciousness in Non-Responsive Patients
FP7 - 602450 - IMAGEMEND - IMAging GEnetics for MENtal Disorders

Organisme subsidiant :

F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
JSMF - James S McDonnell Foundation [US-MO]
FWB - Fédération Wallonie-Bruxelles [BE]
ULiège - Université de Liège [BE]
CE - Commission Européenne [BE]

Disponible sur ORBi :

depuis le 04 janvier 2019

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318 (dont 21 ULiège)

Nombre de téléchargements

18 (dont 15 ULiège)

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