Publication:
Unveiling the relationships between seismocardiogram signals, physical activity types and metabolic equivalent of task scores

dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.kuauthorGürsoy, Beren Semiz
dc.contributor.kuauthorTokmak, Fadime
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2024-12-29T09:37:47Z
dc.date.issued2023
dc.description.abstractObjective: The diagnosis of metabolic syndrome and cardiovascular disorders can highly benefit from physical activity and energy expenditure assessment. In this study, we investigated the relationship between metabolic equivalent of task (MET) scores and seismocardiogram (SCG)-derived parameters. Methods: We worked with the PAMAP2 dataset and focused on the 3-axial chest acceleration data. We first segmented the 3-axial SCG signals into respiration (0-1 Hz), cardiac vibrations (1-20 Hz) and heart sounds (20-40 Hz) components. Additionally, we investigated their combinations: 0-20 Hz, 1-40 Hz and 0-40 Hz. We then windowed each signal, and extracted time and frequency domain features from each window. Using the MET scores and activity types, we trained linear regression and random forest classification models first using 80-20% split, then with leave-one-subject-out cross-validation (LOSO-CV). Additionally, we investigated the significance of each feature and axis. Results: For the 80-20% task, the best performing frequency bands were 0-1 Hz, 0-20 Hz, and 0-40 Hz, which yielded a (MET mean-squared-error, classification accuracy) pair of (0.354, 0.952), (0.367, 0.904), and (0.377, 0.914), respectively. When LOSO-CV was applied, we obtained (1.059, 0.865), (0.681, 0.868), and (0.804, 0.875) for each band, respectively. Additionally, our results revealed that the lateral axis provides the most critical information about cardiorespiratory effect of performed activities. Conclusion: Different SCG components can provide unique and substantial contributions to activity and energy expenditure assessment. Significance: This framework can be leveraged in the design of wearable systems for monitoring the activity and energy expenditure levels, and understanding their relationship with underlying cardiorespiratory parameters.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue2
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.volume70
dc.identifier.doi10.1109/TBME.2022.3194594
dc.identifier.eissn1558-2531
dc.identifier.issn0018-9294
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85135737321
dc.identifier.urihttps://doi.org/10.1109/TBME.2022.3194594
dc.identifier.urihttps://hdl.handle.net/20.500.14288/22485
dc.identifier.wos966007700001
dc.keywordsFeature extraction
dc.keywordsTask analysis
dc.keywordsHeart
dc.keywordsMonitoring
dc.keywordsBiomedical monitoring
dc.keywordsAccelerometers
dc.keywordsVibrations
dc.keywordsActivity monitoring
dc.keywordsMetabolic equivalent of task (MET)
dc.keywordsSeismocardiogram (SCG)
dc.language.isoeng
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIEEE Transactions on Biomedical Engineering
dc.rightsCC BY-NC-ND (Attribution-NonCommercial-NoDerivs)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBiomedical engineering
dc.titleUnveiling the relationships between seismocardiogram signals, physical activity types and metabolic equivalent of task scores
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorTokmak, Fadime
local.contributor.kuauthorGürsoy, Beren Semiz
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Computer Engineering;Department of Electrical and Electronics Engineering
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relation.isOrgUnitOfPublication21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isOrgUnitOfPublication.latestForDiscovery89352e43-bf09-4ef4-82f6-6f9d0174ebae
relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
relation.isParentOrgUnitOfPublication.latestForDiscovery8e756b23-2d4a-4ce8-b1b3-62c794a8c164

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