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Children’s Pain Identification Based on Skin Potential Signal

Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper,...

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Autores principales: Li, Yubo, He, Jiadong, Fu, Cangcang, Jiang, Ke, Cao, Junjie, Wei, Bing, Wang, Xiaozhi, Luo, Jikui, Xu, Weize, Zhu, Jihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422611/
https://www.ncbi.nlm.nih.gov/pubmed/37571601
http://dx.doi.org/10.3390/s23156815
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author Li, Yubo
He, Jiadong
Fu, Cangcang
Jiang, Ke
Cao, Junjie
Wei, Bing
Wang, Xiaozhi
Luo, Jikui
Xu, Weize
Zhu, Jihua
author_facet Li, Yubo
He, Jiadong
Fu, Cangcang
Jiang, Ke
Cao, Junjie
Wei, Bing
Wang, Xiaozhi
Luo, Jikui
Xu, Weize
Zhu, Jihua
author_sort Li, Yubo
collection PubMed
description Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future.
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spelling pubmed-104226112023-08-13 Children’s Pain Identification Based on Skin Potential Signal Li, Yubo He, Jiadong Fu, Cangcang Jiang, Ke Cao, Junjie Wei, Bing Wang, Xiaozhi Luo, Jikui Xu, Weize Zhu, Jihua Sensors (Basel) Article Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future. MDPI 2023-07-31 /pmc/articles/PMC10422611/ /pubmed/37571601 http://dx.doi.org/10.3390/s23156815 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Yubo
He, Jiadong
Fu, Cangcang
Jiang, Ke
Cao, Junjie
Wei, Bing
Wang, Xiaozhi
Luo, Jikui
Xu, Weize
Zhu, Jihua
Children’s Pain Identification Based on Skin Potential Signal
title Children’s Pain Identification Based on Skin Potential Signal
title_full Children’s Pain Identification Based on Skin Potential Signal
title_fullStr Children’s Pain Identification Based on Skin Potential Signal
title_full_unstemmed Children’s Pain Identification Based on Skin Potential Signal
title_short Children’s Pain Identification Based on Skin Potential Signal
title_sort children’s pain identification based on skin potential signal
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422611/
https://www.ncbi.nlm.nih.gov/pubmed/37571601
http://dx.doi.org/10.3390/s23156815
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