TY  - JOUR
AU  - Santala, Onni E
AU  - Lipponen, Jukka A
AU  - Jäntti, Helena
AU  - Rissanen, Tuomas T
AU  - Tarvainen, Mika P
AU  - Laitinen, Tomi P
AU  - Laitinen, Tiina M
AU  - Castrén, Maaret
AU  - Väliaho, Eemu-Samuli
AU  - Rantula, Olli A
AU  - Naukkarinen, Noora S
AU  - Hartikainen, Juha E K
AU  - Halonen, Jari
AU  - Martikainen, Tero J
PY  - 2022
DA  - 2022/6/21
TI  - Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study
JO  - JMIR Cardio
SP  - e31230
VL  - 6
IS  - 1
KW  - atrial fibrillation
KW  - heart rate variability
KW  - HRV
KW  - algorithm
KW  - stroke
KW  - mobile health
KW  - mHealth
KW  - Awario analysis Service, screening
KW  - risk
KW  - stroke risk
KW  - heart rate
KW  - feasibility
KW  - reliability
KW  - artificial intelligence
KW  - mobile patch
KW  - wearable
KW  - arrhythmia
KW  - screening
AB  - 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 
SN  - 2561-1011
UR  - https://cardio.jmir.org/2022/1/e31230
UR  - https://doi.org/10.2196/31230
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35727618
DO  - 10.2196/31230
ID  - info:doi/10.2196/31230
ER  -