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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...

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Autores principales: Fernandez Rojas, Raul, Hirachan, Niraj, Brown, Nicholas, Waddington, Gordon, Murtagh, Luke, Seymour, Ben, Goecke, Roland
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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.
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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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