Diagnostic Accuracy of a Novel Mobile Phone Application for the Detection and Monitoring of Atrial Fibrillation

Background Atrial fibrillation (AF) is the most common tachyarrhythmia and associated with a risk of stroke. The detection and diagnosis of AF represent a major clinical challenge due to AF’s asymptomatic and intermittent nature. Novel consumer-grade mobile health (mHealth) products with automatic arrhythmia detection could be an option for long-term electrocardiogram (ECG)-based rhythm monitoring and AF detection. Objective We evaluated the feasibility and accuracy of a wearable automated mHealth arrhythmia monitoring system, including a consumer-grade, single-lead heart rate belt ECG device (heart belt), a mobile phone application, and a cloud service with an artificial intelligence (AI) arrhythmia detection algorithm for AF detection. The specific aim of this proof-of-concept study was to test the feasibility of the entire sequence of operations from ECG recording to AI arrhythmia analysis and ultimately to final AF detection. Methods Patients (n=159) with an AF (n=73) or sinus rhythm (n=86) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 hours. Simultaneously registered 3-lead ECGs (Holter) served as the gold standard for the final rhythm diagnostics and as a reference device in a user experience survey with patients over 65 years of age (high-risk group). Results The heart belt provided a high-quality ECG recording for visual interpretation resulting in 100% accuracy, sensitivity, and specificity of AF detection. The accuracy of AF detection with the automatic AI arrhythmia detection from the heart belt ECG recording was also high (97.5%), and the sensitivity and specificity were 100% and 95.4%, respectively. The correlation between the automatic estimated AF burden and the true AF burden from Holter recording was >0.99 with a mean burden error of 0.05 (SD 0.26) hours. The heart belt demonstrated good user experience and did not significantly interfere with the patient’s daily activities. The patients preferred the heart belt over Holter ECG for rhythm monitoring (85/110, 77% heart belt vs 77/109, 71% Holter, P=.049). Conclusions A consumer-grade, single-lead ECG heart belt provided good-quality ECG for rhythm diagnosis. The mHealth arrhythmia monitoring system, consisting of heart-belt ECG, a mobile phone application, and an automated AF detection achieved AF detection with high accuracy, sensitivity, and specificity. In addition, the mHealth arrhythmia monitoring system showed good user experience. Trial Registration ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335

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Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

JMIR mhealth and uhealth ◽

10.2196/12284 ◽

2019 ◽

Vol 7 (3) ◽

pp. e12284 ◽

Cited By ~ 19

Author(s):

Tine Proesmans ◽

Christophe Mortelmans ◽

Ruth Van Haelst ◽

Frederik Verbrugge ◽

Pieter Vandervoort ◽

...

Keyword(s):

Atrial Fibrillation ◽

Primary Care ◽

Diagnostic Accuracy ◽

Mobile Phone ◽

Diagnostic Accuracy Study ◽

Accuracy Study

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2247Diagnostic accuracy of a novel mobile phone application in monitoring atrial fibrillation

European Heart Journal ◽

10.1093/eurheartj/ehx502.2247 ◽

2017 ◽

Vol 38 (suppl_1) ◽

Author(s):

G. Rozen ◽

J. Vaid ◽

S.M. Hosseini ◽

A. Rafael ◽

A. Roka ◽

...

Keyword(s):

Atrial Fibrillation ◽

Mobile Phone ◽

Mobile Phone Application

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Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App (Preprint)

10.2196/preprints.12284 ◽

2018 ◽

Author(s):

Tine Proesmans ◽

Christophe Mortelmans ◽

Ruth Van Haelst ◽

Frederik Verbrugge ◽

Pieter Vandervoort ◽

...

Keyword(s):

Atrial Fibrillation ◽

Primary Care ◽

Diagnostic Accuracy ◽

Mobile Phone ◽

Low Cost ◽

Attractive Alternative ◽

Signal Quality ◽

Convenience Sample ◽

Ecg Signals ◽

Accuracy Study

BACKGROUND Mobile phone apps using photoplethysmography (PPG) technology through their built-in camera are becoming an attractive alternative for atrial fibrillation (AF) screening because of their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy remain to be answered. OBJECTIVE This study tested the diagnostic accuracy of the FibriCheck AF algorithm for the detection of AF on the basis of mobile phone PPG and single-lead electrocardiography (ECG) signals. METHODS A convenience sample of patients aged 65 years and above, with or without a known history of AF, was recruited from 17 primary care facilities. Patients with an active pacemaker rhythm were excluded. A PPG signal was obtained with the rear camera of an iPhone 5S. Simultaneously, a single‑lead ECG was registered using a dermal patch with a wireless connection to the same mobile phone. PPG and single-lead ECG signals were analyzed using the FibriCheck AF algorithm. At the same time, a 12‑lead ECG was obtained and interpreted offline by independent cardiologists to determine the presence of AF. RESULTS A total of 45.7% (102/223) subjects were having AF. PPG signal quality was sufficient for analysis in 93% and single‑lead ECG quality was sufficient in 94% of the participants. After removing insufficient quality measurements, the sensitivity and specificity were 96% (95% CI 89%-99%) and 97% (95% CI 91%-99%) for the PPG signal versus 95% (95% CI 88%-98%) and 97% (95% CI 91%-99%) for the single‑lead ECG, respectively. False-positive results were mainly because of premature ectopic beats. PPG and single‑lead ECG techniques yielded adequate signal quality in 196 subjects and a similar diagnosis in 98.0% (192/196) subjects. CONCLUSIONS The FibriCheck AF algorithm can accurately detect AF on the basis of mobile phone PPG and single-lead ECG signals in a primary care convenience sample.

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