Detecting Atrial Fibrillation With a Wearable Device

dc.contributor.authorSandelin Jonas
dc.contributor.authorSirkiä Jukka-Pekka
dc.contributor.authorAnzanpour Arman
dc.contributor.authorKoivisto Tero
dc.contributor.organizationfi=terveysteknologia|en=Health Technology|
dc.contributor.organization-code1.2.246.10.2458963.20.28696315432
dc.converis.publication-id178083784
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/178083784
dc.date.accessioned2025-08-27T12:30:29Z
dc.date.available2025-08-27T12:30:29Z
dc.description.abstract<p>Atrial fibrillation (AFib) is the most common heart arrhythmia in the world but detecting it can be challenging. For this reason, a detection system consisting of a wearable electrocardiogram (ECG) device, a smart phone application and an algorithm was created. The wearable device was designed to be aesthetically simple yet attractive and be worn either as a necklace or a keychain so that it would always be within reach. The overall usability was also a design goal from the start, requiring the user to only touch the device and start a measurement from the smartphone application with a press of a button. The recorded data was processed with an AFib detection algorithm created based on the Chapman university’s database with over 10,000 patients with different heart rhythms. The algorithm is a rule-based detection method, which uses heart rate variability and auto-correlation features. Motion artifacts were also taken into account by using an accelerometer signal measured with the device. The algorithm had an accuracy of 95.3% for the original database while all of the healthy volunteers (n = 14) tested with the developed system were correctly predicted to have sinus rhythms. The aim is to continue the study by increasing the test set size and to measure ECG with the device from AFib patients.<br></p>
dc.identifier.issn2325-8861
dc.identifier.jour-issn2325-8861
dc.identifier.olddbid199840
dc.identifier.oldhandle10024/182867
dc.identifier.urihttps://www.utupub.fi/handle/11111/44349
dc.identifier.urlhttps://cinc.org/archives/2022/pdf/CinC2022-184.pdf
dc.identifier.urnURN:NBN:fi-fe202301265917
dc.language.isoen
dc.okm.affiliatedauthorSandelin, Jonas
dc.okm.affiliatedauthorSirkiä, Jukka-Pekka
dc.okm.affiliatedauthorAnzanpour, Arman
dc.okm.affiliatedauthorKoivisto, Tero
dc.okm.discipline113 Computer and information sciencesen_GB
dc.okm.discipline217 Medical engineeringen_GB
dc.okm.discipline3141 Health care scienceen_GB
dc.okm.discipline113 Tietojenkäsittely ja informaatiotieteetfi_FI
dc.okm.discipline217 Lääketieteen tekniikkafi_FI
dc.okm.discipline3141 Terveystiedefi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA4 Conference Article
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.conferenceComputing in Cardiology
dc.relation.doi10.22489/CinC.2022.184
dc.relation.ispartofjournalComputing in Cardiology
dc.relation.ispartofseriesComputing in Cardiology
dc.relation.volume49
dc.source.identifierhttps://www.utupub.fi/handle/10024/182867
dc.titleDetecting Atrial Fibrillation With a Wearable Device
dc.title.bookComputing in Cardiology 2022
dc.year.issued2022

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