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

ObjectiveWe assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain. MethodsThirty-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-mont...

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Bibliographic Details
Published in:Frontiers in pain research (Lausanne, Switzerland) Switzerland), 2022-09, Vol.3, p.991793-991793
Main Authors: Teel, Elizabeth F., Ocay, Don Daniel, Blain-Moraes, Stefanie, Ferland, Catherine E.
Format: Article
Language:English
Subjects:
children
EEG
machine learning
neuroimaging
pain
Pain Research
sensory testing
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Summary:ObjectiveWe assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain. MethodsThirty-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. ResultsSVM models significantly distinguished between baseline and CPT conditions in chronic pain (75.2% accuracy, 95% CI: 71.4%-77.1%; p 
ISSN:2673-561X
2673-561X
DOI:10.3389/fpain.2022.991793