Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation.

Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.

Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study.

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.