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-month...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Pain Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpain.2022.991793/full |
| _version_ | 1811258381161201664 |
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| author | Elizabeth F. Teel Don Daniel Ocay Don Daniel Ocay Stefanie Blain-Moraes Stefanie Blain-Moraes Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland |
| author_facet | Elizabeth F. Teel Don Daniel Ocay Don Daniel Ocay Stefanie Blain-Moraes Stefanie Blain-Moraes Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland |
| author_sort | Elizabeth F. Teel |
| collection | DOAJ |
| description | 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 < 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.ConclusionsOur 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. |
| first_indexed | 2024-04-12T18:12:33Z |
| format | Article |
| id | doaj.art-dbf3874e599e47dfa3613a3ee78e9d6b |
| institution | Directory Open Access Journal |
| issn | 2673-561X |
| language | English |
| last_indexed | 2024-04-12T18:12:33Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pain Research |
| spelling | doaj.art-dbf3874e599e47dfa3613a3ee78e9d6b2022-12-22T03:21:46ZengFrontiers Media S.A.Frontiers in Pain Research2673-561X2022-09-01310.3389/fpain.2022.991793991793Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal painElizabeth F. Teel0Don Daniel Ocay1Don Daniel Ocay2Stefanie Blain-Moraes3Stefanie Blain-Moraes4Catherine E. Ferland5Catherine E. Ferland6Catherine E. Ferland7Catherine E. Ferland8Catherine E. Ferland9Department of Health, Kinesiology, & Applied Physiology, School of Physical and Occupational Therapy, McGill University, Montreal, QC, CanadaDepartment of Experimental Surgery, McGill University, Montreal, QC, CanadaShriners Hospitals for Children-Canada, Montreal, QC, CanadaMontreal General Hospital, McGill University Health Centre, Montreal, QC, CanadaSchool of Physical and Occupational Therapy, McGill University, Montreal, QC, CanadaShriners Hospitals for Children-Canada, Montreal, QC, CanadaMontreal General Hospital, McGill University Health Centre, Montreal, QC, CanadaDepartment of Anesthesia, McGill University, Montreal, QC, CanadaResearch Institute-McGill University Health Centre, Montreal, QC, CanadaAlan Edwards Research Center for Pain, McGill University, Montreal, QC, CanadaObjectiveWe 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 < 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.ConclusionsOur 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.https://www.frontiersin.org/articles/10.3389/fpain.2022.991793/fullpainmachine learningchildrenEEGsensory testingneuroimaging |
| spellingShingle | Elizabeth F. Teel Don Daniel Ocay Don Daniel Ocay Stefanie Blain-Moraes Stefanie Blain-Moraes Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland Catherine E. Ferland Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain Frontiers in Pain Research pain machine learning children EEG sensory testing neuroimaging |
| 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 machine learning children EEG sensory testing neuroimaging |
| url | https://www.frontiersin.org/articles/10.3389/fpain.2022.991793/full |
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