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dc.contributor.authorEskandari, Maryam
dc.contributor.authorParvaneh, Saman
dc.contributor.authorEhsani, Hossein
dc.contributor.authorFain, Mindy
dc.contributor.authorToosizadeh, Nima
dc.date.accessioned2022-08-04T21:41:36Z
dc.date.available2022-08-04T21:41:36Z
dc.date.issued2022-07-01
dc.identifier.citationEskandari-Nojehdehi, M., Parvaneh, S., Ehsani, H., Fain, M., & Toosizadeh, N. (2022). Frailty Identification using Heart Rate Dynamics: A Deep Learning Approach. IEEE Journal of Biomedical and Health Informatics.en_US
dc.identifier.pmid35196247
dc.identifier.doi10.1109/JBHI.2022.3152538
dc.identifier.urihttp://hdl.handle.net/10150/665543
dc.description.abstractPrevious research showed that frailty can influence autonomic nervous system and consequently heart rate response to physical activities, which can ultimately influence the homeostatic state among older adults. While most studies have focused on resting state heart rate characteristics or heart rate monitoring without controlling for physical activities, the objective of the current study was to classify pre-frail/frail vs non-frail older adults using heart rate response to physical activity (heart rate dynamics). Eighty-eight older adults (65 years) were recruited and stratified into frailty groups based on the five-component Fried frailty phenotype. Groups consisted of 27 non-frail (age=78.807.23) and 61 pre-frail/frail (age=80.638.07) individuals. Participants performed a normal speed walking as the physical task, while heart rate was measured using a wearable electrocardiogram recorder. After creating heart rate time series, a long-short term memory model was used to classify participants into frailty groups. In 5-fold cross validation evaluation, the long-short term memory model could classify the two above-mentioned frailty classes with a sensitivity, specificity, F1-score, and accuracy of 83.0%, 80.0%, 87.0%, and 82.0%, respectively. These findings showed that heart rate dynamics classification using long-short term memory without any feature engineering may provide an accurate and objective marker for frailty screening.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rights© 2022 IEEE.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectAgingen_US
dc.subjectClassificationen_US
dc.subjectData augmentationen_US
dc.subjectDeep learningen_US
dc.subjectFrailtyen_US
dc.subjectHeart rate variabilityen_US
dc.subjectLong short-term memoryen_US
dc.subjectMachine learningen_US
dc.titleFrailty Identification Using Heart Rate Dynamics: A Deep Learning Approachen_US
dc.typeArticleen_US
dc.identifier.eissn2168-2208
dc.contributor.departmentComputer Science, The University of Arizonaen_US
dc.contributor.departmentDepartment of Medicine, The University of Arizonaen_US
dc.contributor.departmentBiomedical Engineering, The University of Arizonaen_US
dc.identifier.journalIEEE journal of biomedical and health informaticsen_US
dc.description.noteImmediate accessen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleIEEE journal of biomedical and health informatics
dc.source.volume26
dc.source.issue7
dc.source.beginpage3409
dc.source.endpage3417
refterms.dateFOA2022-08-04T21:41:38Z
dc.source.countryUnited States
dc.source.countryUnited States


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