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Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines

BACKGROUND: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are r...

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Autores principales: Gruss, Sascha, Treister, Roi, Werner, Philipp, Traue, Harald C., Crawcour, Stephen, Andrade, Adriano, Walter, Steffen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4608770/
https://www.ncbi.nlm.nih.gov/pubmed/26474183
http://dx.doi.org/10.1371/journal.pone.0140330
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author Gruss, Sascha
Treister, Roi
Werner, Philipp
Traue, Harald C.
Crawcour, Stephen
Andrade, Adriano
Walter, Steffen
author_facet Gruss, Sascha
Treister, Roi
Werner, Philipp
Traue, Harald C.
Crawcour, Stephen
Andrade, Adriano
Walter, Steffen
author_sort Gruss, Sascha
collection PubMed
description BACKGROUND: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. METHODS: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. RESULTS: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. CONCLUSION: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.
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spelling pubmed-46087702015-10-29 Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines Gruss, Sascha Treister, Roi Werner, Philipp Traue, Harald C. Crawcour, Stephen Andrade, Adriano Walter, Steffen PLoS One Research Article BACKGROUND: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. METHODS: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. RESULTS: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. CONCLUSION: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment. Public Library of Science 2015-10-16 /pmc/articles/PMC4608770/ /pubmed/26474183 http://dx.doi.org/10.1371/journal.pone.0140330 Text en © 2015 Gruss et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gruss, Sascha
Treister, Roi
Werner, Philipp
Traue, Harald C.
Crawcour, Stephen
Andrade, Adriano
Walter, Steffen
Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines
title Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines
title_full Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines
title_fullStr Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines
title_full_unstemmed Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines
title_short Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines
title_sort pain intensity recognition rates via biopotential feature patterns with support vector machines
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4608770/
https://www.ncbi.nlm.nih.gov/pubmed/26474183
http://dx.doi.org/10.1371/journal.pone.0140330
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