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Accepted for/Published in: JMIR mHealth and uHealth

Date Submitted: Apr 26, 2021
Date Accepted: Aug 27, 2021

The final, peer-reviewed published version of this preprint can be found here:

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

Santala OE, Halonen J, Martikainen S, Jäntti H, Rissanen TT, Tarvainen MP, Laitinen TP, Laitinen TM, Väliaho ES, Hartikainen JEK, Martikainen T, Lipponen JA

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

JMIR Mhealth Uhealth 2021;9(10):e29933

DOI: 10.2196/29933

PMID: 34677135

PMCID: 8571685

Feasibility, accuracy, and user experience of automatic mobile health arrhythmia monitoring for the detection of atrial fibrillation: A prospective feasibility study

  • Onni Elmeri Santala; 
  • Jari Halonen; 
  • Susanna Martikainen; 
  • Helena Jäntti; 
  • Tuomas Tapio Rissanen; 
  • Mika Petteri Tarvainen; 
  • Tomi Petteri Laitinen; 
  • Tiina Marja Laitinen; 
  • Eemu-Samuli Väliaho; 
  • Juha Erkki Kalle Hartikainen; 
  • Tero Martikainen; 
  • Jukka Antero Lipponen

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 the final AF detection.

Methods:

Patients (n=159) with an AF (n=75) or sinus rhythm (n=84) were recruited from the emergency department. A single-lead heart belt ECG was recorded for 24 h. Simultaneously registered 3-lead ECGs (Holter) served as the golden standard for the final rhythm diagnostics and as a reference device in a user experience survey in patients over 65 years of age (high-risk group).

Results:

The heart belt provided 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 burden error of 0.05 h ± 0.26 h (mean ± SD). 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 (heart belt 77% vs. Holter 71%).

Conclusions:

A consumer-grade single-lead ECG heart belt provided good quality ECG for rhythm diagnosis. The mHealth 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 system showed good user experience. Clinical Trial: ClinicalTrials.gov Identifier: NCT03507335


 Citation

Please cite as:

Santala OE, Halonen J, Martikainen S, Jäntti H, Rissanen TT, Tarvainen MP, Laitinen TP, Laitinen TM, Väliaho ES, Hartikainen JEK, Martikainen T, Lipponen JA

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

JMIR Mhealth Uhealth 2021;9(10):e29933

DOI: 10.2196/29933

PMID: 34677135

PMCID: 8571685

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