@Article{info:doi/10.2196/31230,
author="Santala, Onni E
and Lipponen, Jukka A
and J{\"a}ntti, Helena
and Rissanen, Tuomas T
and Tarvainen, Mika P
and Laitinen, Tomi P
and Laitinen, Tiina M
and Castr{\'e}n, Maaret
and V{\"a}liaho, Eemu-Samuli
and Rantula, Olli A
and Naukkarinen, Noora S
and Hartikainen, Juha E K
and Halonen, Jari
and Martikainen, Tero J",
title="Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study",
journal="JMIR Cardio",
year="2022",
month="Jun",
day="21",
volume="6",
number="1",
pages="e31230",
keywords="atrial fibrillation; heart rate variability; HRV; algorithm; stroke; mobile health; mHealth; Awario analysis Service, screening; risk; stroke risk; heart rate; feasibility; reliability; artificial intelligence; mobile patch; wearable; arrhythmia; screening",
abstract="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 ",
issn="2561-1011",
doi="10.2196/31230",
url="https://cardio.jmir.org/2022/1/e31230",
url="https://doi.org/10.2196/31230",
url="http://www.ncbi.nlm.nih.gov/pubmed/35727618"
}