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Identifying Biomarkers for Accurate Detection of Stress

Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this ex...

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Autores principales: Jambhale, Kiran, Mahajan, Smridhi, Rieland, Benjamin, Banerjee, Nilanjan, Dutt, Abhijit, Kadiyala, Sai Praveen, Vinjamuri, Ramana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697543/
https://www.ncbi.nlm.nih.gov/pubmed/36433299
http://dx.doi.org/10.3390/s22228703
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author Jambhale, Kiran
Mahajan, Smridhi
Rieland, Benjamin
Banerjee, Nilanjan
Dutt, Abhijit
Kadiyala, Sai Praveen
Vinjamuri, Ramana
author_facet Jambhale, Kiran
Mahajan, Smridhi
Rieland, Benjamin
Banerjee, Nilanjan
Dutt, Abhijit
Kadiyala, Sai Praveen
Vinjamuri, Ramana
author_sort Jambhale, Kiran
collection PubMed
description Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD.
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spelling pubmed-96975432022-11-26 Identifying Biomarkers for Accurate Detection of Stress Jambhale, Kiran Mahajan, Smridhi Rieland, Benjamin Banerjee, Nilanjan Dutt, Abhijit Kadiyala, Sai Praveen Vinjamuri, Ramana Sensors (Basel) Article Substance use disorder (SUD) is a dangerous epidemic that develops out of recurrent use of alcohol and/or drugs and has the capability to severely damage one’s brain and behaviour. Stress is an established risk factor in SUD’s development of addiction and in reinstating drug seeking. Despite this expanding epidemic and the potential for its grave consequences, there are limited options available for management and treatment, as well as pharmacotherapies and psychosocial treatments. To this end, there is a need for new and improved devices dedicated to the detection, management, and treatment of SUD. In this paper, the negative effects of SUD-related stress were discussed, and based on that, a few significant biomarkers were selected from a set of eight features collected by a chest-worn device, RespiBAN Professional, on fifteen individuals. We used three machine learning classifiers on these optimal biomarkers to detect stress. Based on the accuracies, the best biomarkers to detect stress and those considered as features for classification were determined to be electrodermal activity (EDA), body temperature, and a chest-worn accelerometer. Additionally, the differences between mental stress and physical stress, as well as different administrations of meditation during the study, were identified and analysed. Challenges, implications, and applications were also discussed. In the near future, we aim to replicate the proposed methods in individuals with SUD. MDPI 2022-11-11 /pmc/articles/PMC9697543/ /pubmed/36433299 http://dx.doi.org/10.3390/s22228703 Text en © 2022 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
Jambhale, Kiran
Mahajan, Smridhi
Rieland, Benjamin
Banerjee, Nilanjan
Dutt, Abhijit
Kadiyala, Sai Praveen
Vinjamuri, Ramana
Identifying Biomarkers for Accurate Detection of Stress
title Identifying Biomarkers for Accurate Detection of Stress
title_full Identifying Biomarkers for Accurate Detection of Stress
title_fullStr Identifying Biomarkers for Accurate Detection of Stress
title_full_unstemmed Identifying Biomarkers for Accurate Detection of Stress
title_short Identifying Biomarkers for Accurate Detection of Stress
title_sort identifying biomarkers for accurate detection of stress
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9697543/
https://www.ncbi.nlm.nih.gov/pubmed/36433299
http://dx.doi.org/10.3390/s22228703
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