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Electrodermal Activity Based Pre-surgery Stress Detection Using a Wrist Wearable

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cris.virtual.author-orcid0000-0002-6714-9147
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cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.departmentIndian Institute of Technology, Madras
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cris.virtualsource.author-orcide6b935f2-d800-41e3-b9de-62cbe1725a18
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cris.virtualsource.departmente6b935f2-d800-41e3-b9de-62cbe1725a18
dc.contributor.authorAnusha, A. S.
dc.contributor.authorSukumaran, P.
dc.contributor.authorSarveswaran, V.
dc.contributor.authorSurees Kumar, S.
dc.contributor.authorShyam, A.
dc.contributor.authorAkl, Tony J.
dc.contributor.authorPreejith, S. P.
dc.contributor.authorSivaprakasam, Mohanasankar
dc.date.accessioned2023-09-19T13:31:44Z
dc.date.available2023-09-19T13:31:44Z
dc.date.issued01-01-2020
dc.description.abstractSurgery is a particularly potent stressor and the detrimental effects of stress on people undergoing any surgery is indisputable. When left unchecked, the pre-surgery stress adversely impacts people's physical and psychological well-being, and may even evolve into severe pathological states. Therefore, it is essential to identify levels of preoperative stress in surgical patients. This paper focuses on developing an automatic pre-surgery stress detection scheme based on electrodermal activity (EDA). The measurement set up involves a wrist wearable that monitors EDA of a subject continuously in the most non-invasive and unobtrusive manner. Data were collected from 41 subjects [17 females and 24 males, age: 54.8 pm 16.8 years (mean pm SD)], who subsequently underwent different surgical procedures at the Sri Ramakrishna Hospital, Coimbatore, India. A supervised machine learning algorithm that detects motion artifacts in the recorded EDA data was developed. It yielded an accuracy of 97.83% on a new user dataset. The clean EDA data were further analyzed to determine low, moderate, and high levels of stress. A novel localized supervised learning scheme based on the adaptive partitioning of the dataset was adopted for stress detection. Consequently, the interindividual variability in the EDA due to person-specific factors such as the sweat gland density and skin thickness, which may lead to erroneous classification, could be eliminated. The scheme yielded a classification accuracy of 85.06% on a new user dataset and proved to be more effective than the general supervised classification model.
dc.identifier.doi10.1109/JBHI.2019.2893222
dc.identifier.issn21682194
dc.identifier.pmid30668508
dc.identifier.scopus2-s2.0-85077545569
dc.identifier.urihttps://apicris.irins.org/handle/IITM2023/20145
dc.relation.ispartofseriesIEEE Journal of Biomedical and Health Informatics
dc.sourceIEEE Journal of Biomedical and Health Informatics
dc.subjectElectrodermal activity (EDA)
dc.subjectpre-surgery stress
dc.subjectstress detection
dc.subjectwearables
dc.titleElectrodermal Activity Based Pre-surgery Stress Detection Using a Wrist Wearable
dc.typeJournal
dspace.entity.typePublication
oaire.citation.endPage100
oaire.citation.issue1
oaire.citation.startPage92
oaire.citation.volume24
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oairecerif.author.affiliationIndian Institute of Technology, Madras
person.affiliation.cityChennai
person.affiliation.cityNorwood
person.affiliation.cityCoimbatore
person.affiliation.id60025757
person.affiliation.id60023690
person.affiliation.id106398454
person.affiliation.nameIndian Institute of Technology Madras
person.affiliation.nameAnalog Devices, Inc.
person.affiliation.nameSri Ramakrishna Hospital
person.identifier.scopus-author-id57190980044
person.identifier.scopus-author-id57213355695
person.identifier.scopus-author-id6602560386
person.identifier.scopus-author-id57213351246
person.identifier.scopus-author-id57203835932
person.identifier.scopus-author-id36058211200
person.identifier.scopus-author-id56648102700
person.identifier.scopus-author-id6506470218
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