Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study

Abstract

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

Smartphone App in Stroke Management: A Narrative Updated Review

“…There have been several studies demonstrating that smartwatches can detect AF events [ 74 ]. Devices such as belts [ 75 ], miniaturized electrocardiograms (ECGs) associated with smartphone Apps [ 8 , 76 ], and devices that are placed on the patient’s chest have also been used [ 77 , 78 ].…”

“…Moreover, the use of smartphone embedded sensors (gyroscope and accelerometer), along with ad-ditional wearable technologies, provides a constant monitoring of the rehabilitation phases quantifying and recording parameters such as walking, posture, and balance [50][51][52][53][54]81,82 and ensure continuous monitoring of vital parameters with the aim of a more comprehensive management of risk factors for stroke. [74][75][76][77][78] In addition, telemedicine-based technologies allow healthcare providers to remotely track patient's exercise and progress. 60 Teleconsultation, however, is not always possible due to technological and logistical limitations such as the presence of an effective Internet network and the 24-hour presence of an active consultant.…”

“…There have been several studies demonstrating that smartwatches can detect AF events. 74 Devices such as belts, 75 miniaturized electrocardiograms (ECGs) associated with smartphone Apps, 8,76 and devices that are placed on the patient's chest have also been used. 77,78 Another useful App in the management of AF is AFib 2gether mobile App, 79,80 which was developed by Pfizer Inc. (New York, NY, USA).…”

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

“…The diagnostic accuracy of the AI arrhythmia detection algorithm used in this study to detect AF was comparable to other screening methods and devices. In the previous studies, the sensitivity of automatic AF detection ranged from 67% to 100% and the specificity from 84% to 100% depending on the mobile or digital technology and strategy used [ 21 - 36 ]. In our study, the sensitivity and specificity of AF detection were 100% and 94.9%, respectively.…”

Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation

“…Most arrhythmias are short-lived, but the prognostic value of detecting even a few seconds of some of them, for example, asystole or ventricular tachycardia, is very high. The usefulness of opportunistic screening strategies, using an electrocardiogram (ECG) or other methods for random "snapshot" assessments, is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis [7][8][9][10].…”