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Multimodal physiological sensing for the assessment of acute pain
Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients’ self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10321707/ https://www.ncbi.nlm.nih.gov/pubmed/37415829 http://dx.doi.org/10.3389/fpain.2023.1150264 |
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author | Fernandez Rojas, Raul Hirachan, Niraj Brown, Nicholas Waddington, Gordon Murtagh, Luke Seymour, Ben Goecke, Roland |
author_facet | Fernandez Rojas, Raul Hirachan, Niraj Brown, Nicholas Waddington, Gordon Murtagh, Luke Seymour, Ben Goecke, Roland |
author_sort | Fernandez Rojas, Raul |
collection | PubMed |
description | Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients’ self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions, [Formula: see text] in identification of pain, [Formula: see text] in the multiclass problem, and [Formula: see text] for the identification of pain location. These results identify EDA as the superior sensor in our experimental conditions. Future work is required to validate the obtained features to improve its feasibility in more realistic scenarios. Finally, this study proposes EDA as a candidate to design a tool that can assist clinicians in the assessment of acute pain of nonverbal patients. |
format | Online Article Text |
id | pubmed-10321707 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103217072023-07-06 Multimodal physiological sensing for the assessment of acute pain Fernandez Rojas, Raul Hirachan, Niraj Brown, Nicholas Waddington, Gordon Murtagh, Luke Seymour, Ben Goecke, Roland Front Pain Res (Lausanne) Pain Research Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients’ self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions, [Formula: see text] in identification of pain, [Formula: see text] in the multiclass problem, and [Formula: see text] for the identification of pain location. These results identify EDA as the superior sensor in our experimental conditions. Future work is required to validate the obtained features to improve its feasibility in more realistic scenarios. Finally, this study proposes EDA as a candidate to design a tool that can assist clinicians in the assessment of acute pain of nonverbal patients. Frontiers Media S.A. 2023-06-19 /pmc/articles/PMC10321707/ /pubmed/37415829 http://dx.doi.org/10.3389/fpain.2023.1150264 Text en © 2023 Fernandez Rojas, Hirachan, Brown, Waddington, Murtagh, Seymour and Goecke. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pain Research Fernandez Rojas, Raul Hirachan, Niraj Brown, Nicholas Waddington, Gordon Murtagh, Luke Seymour, Ben Goecke, Roland Multimodal physiological sensing for the assessment of acute pain |
title | Multimodal physiological sensing for the assessment of acute pain |
title_full | Multimodal physiological sensing for the assessment of acute pain |
title_fullStr | Multimodal physiological sensing for the assessment of acute pain |
title_full_unstemmed | Multimodal physiological sensing for the assessment of acute pain |
title_short | Multimodal physiological sensing for the assessment of acute pain |
title_sort | multimodal physiological sensing for the assessment of acute pain |
topic | Pain Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10321707/ https://www.ncbi.nlm.nih.gov/pubmed/37415829 http://dx.doi.org/10.3389/fpain.2023.1150264 |
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