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Prediction of Minimally Conscious State Responder Patients to Non-invasive Brain Stimulation Using Machine Learning Algorithms
Rojas, Andrés; Kroupi, Eleni; Martens, Géraldine et al.
2021In Pattern Recognition ICPR International Workshops and Challenges Virtual Event, January 10–15, 2021 Proceedings, Part I
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Keywords :
Artificial intelligence; Decision support systems; Machine learning; Classification approach; Comparative evaluations; Machine learning approaches; Methodological approach; Minimally conscious state; Non-invasive brain stimulations; Transcranial electrical stimulations; Treatment outcomes; Theoretical Computer Science; Computer Science (all)
Abstract :
[en] The right matching of patients to an intended treatment is routinely performed by doctor and physicians in healthcare. Improving doctor’s ability to choose the right treatment can greatly speed up patient’s recovery. In a clinical study on Disorders of Consciousness patients in Minimal Consciousness State (MCS) have gone through transcranial Electrical Stimulation (tES) therapy to increase consciousness level. We have carried out the study of MCS patient’s response to tES therapy using as input the EEG data collected before the intervention. Different Machine Learning approaches have been applied to the Relative Band Power features extracted from the EEG. We aimed to predict tES treatment outcome from this EEG data of 17 patients, where 4 of the patients sustainably showed further signs of consciousness after treatment. We have been able to correctly classify with 95% accuracy the response of patients to tES therapy. In this paper we present the methodology as well as a comparative evaluation of the different employed classification approaches. Hereby we demonstrate the feasibility of implementing a novel informed Decision Support System (DSS) based on this methodological approach for the correct prediction of patients’ response to tES therapy in MCS.
Research center :
CHU de Liège-Centre du Cerveau² - ULiège
Disciplines :
Neurology
Author, co-author :
Rojas, Andrés;  Starlab Barcelona SL, Barcelona, Spain
Kroupi, Eleni;  Starlab Barcelona SL, Barcelona, Spain
Martens, Géraldine  ;  Centre Hospitalier Universitaire de Liège - CHU > > Service de médecine de l'appareil locomoteur
Thibaut, Aurore ;  Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Barra, Alice  ;  Université de Liège - ULiège > GIGA
LAUREYS, Steven  ;  Centre Hospitalier Universitaire de Liège - CHU > > Centre du Cerveau²
Ruffini, Giulio;  Starlab Barcelona SL, Barcelona, Spain
Soria-Frisch, Aureli;  Starlab Barcelona SL, Barcelona, Spain
Language :
English
Title :
Prediction of Minimally Conscious State Responder Patients to Non-invasive Brain Stimulation Using Machine Learning Algorithms
Publication date :
2021
Event name :
Pattern Recognition ICPR International Workshops and Challenges
Event place :
Milan, Italy
Event date :
10-15 January 2021
By request :
Yes
Audience :
International
Main work title :
Pattern Recognition ICPR International Workshops and Challenges Virtual Event, January 10–15, 2021 Proceedings, Part I
Publisher :
Springer
Pages :
515-525
Peer reviewed :
Peer reviewed
Available on ORBi :
since 08 November 2022

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