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Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain

OBJECTIVE: We assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain. METHODS: Thirty-nine healthy controls (15.2 ± 2.1 years, 18 females) and 121 chronic pain participants (15.0 ± 2.0 years, 100 females, 85 experiencing pain ≥12-...

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Autores principales: Teel, Elizabeth F., Ocay, Don Daniel, Blain-Moraes, Stefanie, Ferland, Catherine E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552004/
https://www.ncbi.nlm.nih.gov/pubmed/36238349
http://dx.doi.org/10.3389/fpain.2022.991793
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author Teel, Elizabeth F.
Ocay, Don Daniel
Blain-Moraes, Stefanie
Ferland, Catherine E.
author_facet Teel, Elizabeth F.
Ocay, Don Daniel
Blain-Moraes, Stefanie
Ferland, Catherine E.
author_sort Teel, Elizabeth F.
collection PubMed
description OBJECTIVE: We assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain. METHODS: Thirty-nine healthy controls (15.2 ± 2.1 years, 18 females) and 121 chronic pain participants (15.0 ± 2.0 years, 100 females, 85 experiencing pain ≥12-months) had 19-channel EEG recorded at rest and throughout a cold-pressor task (CPT). Permutation entropy, directed phase lag index, peak frequency, and binary graph theory features were calculated across 10-second EEG epochs (Healthy: 292 baseline / 273 CPT epochs; Pain: 1039 baseline / 755 CPT epochs). Support vector machine (SVM) and logistic regression models were trained to classify between baseline and CPT conditions separately for control and pain participants. RESULTS: SVM models significantly distinguished between baseline and CPT conditions in chronic pain (75.2% accuracy, 95% CI: 71.4%–77.1%; p < 0.0001) and control (74.8% accuracy, 95% CI: 66.3%–77.6%; p < 0.0001) participants. Logistic regression models performed similar to the SVM (Pain: 75.8% accuracy, 95% CI: 69.5%–76.6%, p < 0.0001; Controls: 72.0% accuracy, 95% CI: 64.5%–78.5%, p < 0.0001). Permutation entropy features in the theta frequency band were the largest contributor to model accuracy for both groups. CONCLUSIONS: Our results demonstrate that subjective pain experiences can accurately be detected from electrophysiological data, and represent the first step towards the development of a point-of-care system to detect pain in the absence of self-report.
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spelling pubmed-95520042022-10-12 Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain Teel, Elizabeth F. Ocay, Don Daniel Blain-Moraes, Stefanie Ferland, Catherine E. Front Pain Res (Lausanne) Pain Research OBJECTIVE: We assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain. METHODS: Thirty-nine healthy controls (15.2 ± 2.1 years, 18 females) and 121 chronic pain participants (15.0 ± 2.0 years, 100 females, 85 experiencing pain ≥12-months) had 19-channel EEG recorded at rest and throughout a cold-pressor task (CPT). Permutation entropy, directed phase lag index, peak frequency, and binary graph theory features were calculated across 10-second EEG epochs (Healthy: 292 baseline / 273 CPT epochs; Pain: 1039 baseline / 755 CPT epochs). Support vector machine (SVM) and logistic regression models were trained to classify between baseline and CPT conditions separately for control and pain participants. RESULTS: SVM models significantly distinguished between baseline and CPT conditions in chronic pain (75.2% accuracy, 95% CI: 71.4%–77.1%; p < 0.0001) and control (74.8% accuracy, 95% CI: 66.3%–77.6%; p < 0.0001) participants. Logistic regression models performed similar to the SVM (Pain: 75.8% accuracy, 95% CI: 69.5%–76.6%, p < 0.0001; Controls: 72.0% accuracy, 95% CI: 64.5%–78.5%, p < 0.0001). Permutation entropy features in the theta frequency band were the largest contributor to model accuracy for both groups. CONCLUSIONS: Our results demonstrate that subjective pain experiences can accurately be detected from electrophysiological data, and represent the first step towards the development of a point-of-care system to detect pain in the absence of self-report. Frontiers Media S.A. 2022-09-27 /pmc/articles/PMC9552004/ /pubmed/36238349 http://dx.doi.org/10.3389/fpain.2022.991793 Text en © 2022 Teel, Ocay, Blain-Moraes and Ferland. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pain Research
Teel, Elizabeth F.
Ocay, Don Daniel
Blain-Moraes, Stefanie
Ferland, Catherine E.
Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain
title Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain
title_full Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain
title_fullStr Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain
title_full_unstemmed Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain
title_short Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain
title_sort accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain
topic Pain Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552004/
https://www.ncbi.nlm.nih.gov/pubmed/36238349
http://dx.doi.org/10.3389/fpain.2022.991793
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