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Acute pain intensity monitoring with the classification of multiple physiological parameters

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On th...

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Autores principales: Jiang, Mingzhe, Mieronkoski, Riitta, Syrjälä, Elise, Anzanpour, Arman, Terävä, Virpi, Rahmani, Amir M., Salanterä, Sanna, Aantaa, Riku, Hagelberg, Nora, Liljeberg, Pasi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499869/
https://www.ncbi.nlm.nih.gov/pubmed/29946994
http://dx.doi.org/10.1007/s10877-018-0174-8
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author Jiang, Mingzhe
Mieronkoski, Riitta
Syrjälä, Elise
Anzanpour, Arman
Terävä, Virpi
Rahmani, Amir M.
Salanterä, Sanna
Aantaa, Riku
Hagelberg, Nora
Liljeberg, Pasi
author_facet Jiang, Mingzhe
Mieronkoski, Riitta
Syrjälä, Elise
Anzanpour, Arman
Terävä, Virpi
Rahmani, Amir M.
Salanterä, Sanna
Aantaa, Riku
Hagelberg, Nora
Liljeberg, Pasi
author_sort Jiang, Mingzhe
collection PubMed
description Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.
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spelling pubmed-64998692019-05-20 Acute pain intensity monitoring with the classification of multiple physiological parameters Jiang, Mingzhe Mieronkoski, Riitta Syrjälä, Elise Anzanpour, Arman Terävä, Virpi Rahmani, Amir M. Salanterä, Sanna Aantaa, Riku Hagelberg, Nora Liljeberg, Pasi J Clin Monit Comput Original Research Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain. Springer Netherlands 2018-06-26 2019 /pmc/articles/PMC6499869/ /pubmed/29946994 http://dx.doi.org/10.1007/s10877-018-0174-8 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Research
Jiang, Mingzhe
Mieronkoski, Riitta
Syrjälä, Elise
Anzanpour, Arman
Terävä, Virpi
Rahmani, Amir M.
Salanterä, Sanna
Aantaa, Riku
Hagelberg, Nora
Liljeberg, Pasi
Acute pain intensity monitoring with the classification of multiple physiological parameters
title Acute pain intensity monitoring with the classification of multiple physiological parameters
title_full Acute pain intensity monitoring with the classification of multiple physiological parameters
title_fullStr Acute pain intensity monitoring with the classification of multiple physiological parameters
title_full_unstemmed Acute pain intensity monitoring with the classification of multiple physiological parameters
title_short Acute pain intensity monitoring with the classification of multiple physiological parameters
title_sort acute pain intensity monitoring with the classification of multiple physiological parameters
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499869/
https://www.ncbi.nlm.nih.gov/pubmed/29946994
http://dx.doi.org/10.1007/s10877-018-0174-8
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