Literature DB >> 33838317

Validation of an algorithm for continuous monitoring of atrial fibrillation using a consumer smartwatch.

Robert Avram1, Mattheus Ramsis1, Ashley D Cristal1, Viswam Nathan2, Li Zhu2, Jacob Kim2, Jilong Kuang2, Alex Gao2, Eric Vittinghoff3, Linnea Rohdin-Bibby1, Sara Yogi1, Emina Seremet1, Valerie Carp1, Fabio Badilini4, Mark J Pletcher3, Gregory M Marcus1, David Mortara4, Jeffrey E Olgin5.   

Abstract

BACKGROUND: Consumer devices with broad reach may be useful in screening for atrial fibrillation (AF) in appropriate populations. However, currently no consumer devices are capable of continuous monitoring for AF.
OBJECTIVE: The purpose of this study was to estimate the sensitivity and specificity of a smartwatch algorithm for continuous detection of AF from sinus rhythm in a free-living setting.
METHODS: We studied a commercially available smartwatch with photoplethysmography (W-PPG) and electrocardiogram (W-ECG) capabilities. We validated a novel W-PPG algorithm combined with a W-ECG algorithm in a free-living setting, and compared the results to those of a 28-day continuous ECG patch (P-ECG).
RESULTS: A total of 204 participants completed the free-living study, recording 81,944 hours with both P-ECG and smartwatch measurements. We found sensitivity of 87.8% (95% confidence interval [CI] 83.6%-91.0%) and specificity of 97.4% (95% CI 97.1%-97.7%) for the W-PPG algorithm (every 5-minute classification); sensitivity of 98.9% (95% CI 98.1%-99.4%) and specificity of 99.3% (95% CI 99.1%-99.5%) for the W-ECG algorithm; and sensitivity of 96.9% (95% CI 93.7%-98.5%) and specificity of 99.3% (95% CI 98.4%-99.7%) for W-PPG triggered W-ECG with a single W-ECG required for confirmation of AF. We found a very strong correlation of W-PPG in quantifying AF burden compared to P-ECG (r = 0.98).
CONCLUSION: Our findings demonstrate that a novel algorithm using a commercially available smartwatch can continuously detect AF with excellent performance and that confirmation with W-ECG further enhances specificity. In addition, our W-PPG algorithm can estimate AF burden. Further research is needed to determine whether this algorithm is useful in screening for AF in select at-risk patients.
Copyright © 2021 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Atrial fibrillation; Atrial fibrillation burden; Photoplethysmography Remote monitoring; Screening; Smartwatch

Mesh:

Year:  2021        PMID: 33838317     DOI: 10.1016/j.hrthm.2021.03.044

Source DB:  PubMed          Journal:  Heart Rhythm        ISSN: 1547-5271            Impact factor:   6.343


  9 in total

1.  Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables.

Authors:  Stanisław Saganowski; Joanna Komoszyńska; Maciej Behnke; Bartosz Perz; Dominika Kunc; Bartłomiej Klich; Łukasz D Kaczmarek; Przemysław Kazienko
Journal:  Sci Data       Date:  2022-04-07       Impact factor: 6.444

Review 2.  Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives.

Authors:  Anastasia Xintarakou; Vasileios Sousonis; Dimitrios Asvestas; Panos E Vardas; Stylianos Tzeis
Journal:  Front Cardiovasc Med       Date:  2022-03-01

3.  Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches.

Authors:  Min-Tsun Liao; Chih-Chieh Yu; Lian-Yu Lin; Ke-Han Pan; Tsung-Hsien Tsai; Yu-Chun Wu; Yen-Bin Liu
Journal:  Sci Rep       Date:  2022-03-30       Impact factor: 4.379

4.  Smartwatch Electrocardiograms for Automated and Manual Diagnosis of Atrial Fibrillation: A Comparative Analysis of Three Models.

Authors:  Saer Abu-Alrub; Marc Strik; F Daniel Ramirez; Nadir Moussaoui; Hugo Pierre Racine; Hugo Marchand; Samuel Buliard; Michel Haïssaguerre; Sylvain Ploux; Pierre Bordachar
Journal:  Front Cardiovasc Med       Date:  2022-02-04

5.  Clinical validation of a novel smartwatch for automated detection of atrial fibrillation.

Authors:  Patrick Badertscher; Mirko Lischer; Diego Mannhart; Sven Knecht; Corinne Isenegger; Jeanne Du Fay de Lavallaz; Beat Schaer; Stefan Osswald; Michael Kühne; Christian Sticherling
Journal:  Heart Rhythm O2       Date:  2022-02-08

6.  Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatch.

Authors:  Hyeong Rae Cho; Jin Hyun Kim; Hye Rin Yoon; Yong Seop Han; Tae Seen Kang; Hyunju Choi; Seunghwan Lee
Journal:  Sci Rep       Date:  2022-05-12       Impact factor: 4.996

7.  Remote patient monitoring for chronic heart failure in France: When an innovative funding program (ETAPES) meets an innovative solution (Satelia® Cardio).

Authors:  N Pages; F Picard; F Barritault; W Amara; S Lafitte; P Maribas; P Abassade; J Ph Labarre; R Boulestreau; H Chaouky; M Abdennadher; H Lemieux; R Lasserre; C Bedel; L Betito; S Nisse-Durgeat; B Diebold
Journal:  Digit Health       Date:  2022-08-22

8.  Commercial smartwatch with pulse oximeter detects short-time hypoxemia as well as standard medical-grade device: Validation study.

Authors:  Jakub Rafl; Thomas E Bachman; Veronika Rafl-Huttova; Simon Walzel; Martin Rozanek
Journal:  Digit Health       Date:  2022-10-11

9.  Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation.

Authors:  Eemu-Samuli Väliaho; Jukka A Lipponen; Pekka Kuoppa; Tero J Martikainen; Helena Jäntti; Tuomas T Rissanen; Maaret Castrén; Jari Halonen; Mika P Tarvainen; Tiina M Laitinen; Tomi P Laitinen; Onni E Santala; Olli Rantula; Noora S Naukkarinen; Juha E K Hartikainen
Journal:  Front Physiol       Date:  2022-01-04       Impact factor: 4.566

  9 in total

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