The Performance of Wrist Photoplethysmography in Monitoring Atrial Fibrillation in Post Cardiac Surgery Patients

Adrian Tarniceriu, Vilma Vuohelainen, Serj Haddad, Tuomas Halkola, Jakub Parak, Jari Laurikka, Antti Vehkaoja

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
2 Downloads (Pure)


Background and Aim: Atrial fibrillation (AF) is the most common cardiac arrhythmia, associated with an increased risk of thromboembolic ischemic stroke. Subjects with CHA2DS2-VASc score greater than one have 2.2% or higher annual risk for stroke if not treated with anticoagulant medicine. The presence of AF is normally examined with 24 or 48 h ECG Holter monitoring that is inefficient in case of rarely occurring paroxysmal AF episodes. We evaluated the performance of a wrist-worn photoplethysmografic (PPG) device in monitoring cardiac rhythm and detecting AF. While being comfortable to wear, wrist PPG could provide a solution for continuous 24/7 monitoring. Methods: 30 cardiac surgery patients (9 female, 21 male, 69.3 ± 6.9 years old) were recruited for the study in Cardiac surgery ward at Tampere University Hospital. The subjects were monitored for 24 hours with a wrist-worn PPG monitor (PulseOn Oy, Espoo, Finland) leading to roughly 700 hours of data. 5-lead Holter ECG was used as a reference. The monitoring was started on 2nd to 4th post-operative day and the subjects were mostly staying in bed during the monitoring. The study was approved by the local ethical committee. Inter-beat-intervals (IBI) including signal quality information were estimated from the PPG and further used to detect AF in 5-minute intervals. Results: 12.3 % of the 5-minute segments were discarded due to inadequate signal quality and the remaining data was classified to AF and non-AF. Three out of the 30 subject developed AF during the monitoring period leading to 22 hours of AF data. All data segments during AF were correctly labeled as AF providing 100% sensitivity. From the non-AF data, 96.1% was correctly classified. Most of the incorrect classifications resulted from the presence of very frequent ectopic beats (> 10 per minute). Ignoring these segments improved the specificity to 99.7%.
Original languageEnglish
Title of host publication2019 Computing in Cardiology, CinC 2019
PublisherComputing in Cardiology
Publication statusPublished - 10 Sep 2019
Publication typeA4 Article in conference proceedings
EventComputing in Cardiology -
Duration: 1 Jan 2019 → …

Publication series

NameComputing in cardiology
ISSN (Electronic)2325-887X


ConferenceComputing in Cardiology
Period1/01/19 → …

Publication forum classification

  • Publication forum level 1


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