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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

To test the ability of different entropy measures to classify patients with different conditions of chronic disorder of consciousness, we applied the Lempel–Ziv complexity, the amplitude coalition entropy (ACE), and the synchrony coalition entropy (SCE) to the EEG signals recorded in 32 patients, clinically evaluated using the coma recovery scale revised (CRS-R). All the entropy measures indicated that differences found in the theta and alpha bands can distinguish patients in a minimal consciousness state (MCS) with respect to those in a vegetative state/unresponsive wakefulness state (VS/UWS). These differences were significant comparing the entropy measure performed on the anterior region of the left hemisphere and midline region. The values of theta-alpha entropy positively correlated with those of the CRS-R scores. Among the entropy measures, ACE most often highlighted significant differences. The higher values found in MCS were for the less impaired patients, according to their CRS-R, suggest that the preservation of signal entropy on the anterior region of the dominant hemisphere correlates with better preservation of consciousness, even in chronic conditions.

Details

Title
Entropy Metrics Correlating with Higher Residual Functioning in Patients with Chronic Disorders of Consciousness
Author
Visani, Elisa 1   VIAFID ORCID Logo  ; Luria, Gianvittorio 2 ; Sattin, Davide 3 ; Davide Rossi Sebastiano 1   VIAFID ORCID Logo  ; Ferraro, Stefania 4 ; Panzica, Ferruccio 1 ; Leonardi, Matilde 1   VIAFID ORCID Logo  ; Franceschetti, Silvana 1 

 Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milano, Italy; [email protected] (E.V.); [email protected] (G.L.); [email protected] (D.R.S.); [email protected] (S.F.); [email protected] (F.P.); [email protected] (M.L.) 
 Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milano, Italy; [email protected] (E.V.); [email protected] (G.L.); [email protected] (D.R.S.); [email protected] (S.F.); [email protected] (F.P.); [email protected] (M.L.); Department of Mathematics, University of Genoa, Via Dodecaneso 35, 16146 Genova, Italy 
 IRCCS Istituti Clinici Scientifici Maugeri di Milano, Via Camaldoli 64, 20138 Milano, Italy; [email protected] 
 Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133 Milano, Italy; [email protected] (E.V.); [email protected] (G.L.); [email protected] (D.R.S.); [email protected] (S.F.); [email protected] (F.P.); [email protected] (M.L.); MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology, Chengdu 611731, China 
First page
332
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20763425
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2642344786
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.