| Autor: |
Elizabeth F. Teel, Don Daniel Ocay, Stefanie Blain-Moraes, Catherine E. Ferland |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Frontiers in Pain Research, Vol 3 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2673-561X |
| DOI: |
10.3389/fpain.2022.991793 |
| Popis: |
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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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