Pain phenotypes classified by machine learning using electroencephalography features.
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
30
07
2019
revised:
24
07
2020
accepted:
07
08
2020
pubmed:
2
9
2020
medline:
4
3
2021
entrez:
2
9
2020
Statut:
ppublish
Résumé
Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.
Identifiants
pubmed: 32871260
pii: S1053-8119(20)30742-4
doi: 10.1016/j.neuroimage.2020.117256
pmc: PMC9084327
mid: NIHMS1797911
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
117256Subventions
Organisme : NIGMS NIH HHS
ID : P20 GM103645
Pays : United States
Organisme : NIH HHS
ID : S10 OD025181
Pays : United States
Informations de copyright
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.
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