%0 Journal Article
%@ 2561-1011
%I JMIR Publications
%V 6
%N 1
%P e31230
%T Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study
%A Santala,Onni E
%A Lipponen,Jukka A
%A Jäntti,Helena
%A Rissanen,Tuomas T
%A Tarvainen,Mika P
%A Laitinen,Tomi P
%A Laitinen,Tiina M
%A Castrén,Maaret
%A Väliaho,Eemu-Samuli
%A Rantula,Olli A
%A Naukkarinen,Noora S
%A Hartikainen,Juha E K
%A Halonen,Jari
%A Martikainen,Tero J
%+ School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Yliopistonranta 1 PO BOX 1627, Kuopio, FI-70211, Finland, 358 503010879, elmeris@uef.fi
%K atrial fibrillation
%K heart rate variability
%K HRV
%K algorithm
%K stroke
%K mobile health
%K mHealth
%K Awario analysis Service, screening
%K risk
%K stroke risk
%K heart rate
%K feasibility
%K reliability
%K artificial intelligence
%K mobile patch
%K wearable
%K arrhythmia
%K screening
%D 2022
%7 21.6.2022
%9 Original Paper
%J JMIR Cardio
%G English
%X Background: The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated. Objective: The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being. Methods: Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics. Results: Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively. Conclusions: The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening. Trial Registration: ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335 
%M 35727618
%R 10.2196/31230
%U https://cardio.jmir.org/2022/1/e31230
%U https://doi.org/10.2196/31230
%U http://www.ncbi.nlm.nih.gov/pubmed/35727618