Development of a personalized anomaly detection model to detect motion artifacts over ppg data using catch22 features

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dc.contributor.authorValerio, Andreaen
dc.contributor.authorDemarchi, Daniloen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-01-08T11:28:06Z
dc.date.available2025-01-08T11:28:06Z
dc.date.issued2024en
dc.description.abstractAs remote health monitoring grows, it's crucial to distinguish high-quality biomedical signals from low-quality ones. Identifying and mitigating motion artifacts (MAs) is essential for accurate data from wearable devices. Methods: In this work, a high-performing subset of time-series features denoted as catch22 (22 CAnonical Time-series CHaracteristics) was used to detect the presence of MAs in photoplethysmogram (PPG) data acquired from the brachial and digital artery of 31 healthy subjects. Three unsupervised algorithms were employed along with catch22 to detect MAs within the dataset, these were: One-Class Support Vector Machine, Isolation Forest, and Local Outlier Factor. Results: Aggregated precision, recall, and F1-score were computed per each method to assess the detection performances according to a variety of features and anomalies' distribution. These metrics resulted respectively equal to 0.5, 0.64, and 0.55 for OC- SVM, 0.91, 0.94, and 0.92 for IF, and 0.74, 0.75, and 0.74 for LOF. Conclusion: Experimental findings illustrate that by employing the catch22 feature subset, it is viable to discern the presence of MAs in beat-to-beat pulse waveforms without recurring to prior knowledge or data-driven PPG features.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationValerio, A., Demarchi, D., O’Flynn, B. and Tedesco, S. (2024) ‘Development of a personalized anomaly detection model to detect motion artifacts over ppg data using catch22 features’, IEEE Sensors Journal. https://doi.org/10.1109/SENSORS60989.2024.10785142en
dc.identifier.doihttps://doi.org/10.1109/SENSORS60989.2024.10785142en
dc.identifier.eissn2168-9229en
dc.identifier.endpage4en
dc.identifier.journaltitleIEEE Sensors Journalen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16790
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Maternity/Adoptive Leave Allowance/12/RC/2289-P2s/IE/INSIGHT Phase 2/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3835/IE/VistaMilk Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksen
dc.subjectTrainingen
dc.subjectSupport vector machinesen
dc.subjectForestryen
dc.subjectFeature extractionen
dc.subjectSensorsen
dc.subjectObject recognitionen
dc.subjectWearable devicesen
dc.subjectMonitoringen
dc.subjectAnomaly detectionen
dc.subjectMotion artifactsen
dc.titleDevelopment of a personalized anomaly detection model to detect motion artifacts over ppg data using catch22 featuresen
dc.typeArticle (peer-reviewed)en
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