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Title

A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation

Authors

David D. McManus, University of Massachusetts Medical SchoolFollow
Jinseok Lee, Wonkwang University School of Medicine
Oscar Maitas, University of Massachusetts Medical SchoolFollow
Nada Esa, University of Massachusetts Medical SchoolFollow
Rahul Pidikiti, University of Massachusetts Medical SchoolFollow
Alex Carlucci, University of Massachustts Medical SchoolFollow
Josephine Harrington, University of Massachusetts Medical SchoolFollow
Eric Mick, University of Massachusetts Medical SchoolFollow
Ki H. Chon, Wonkwang University School of Medicine

UMMS Affiliation

Department of Quantitative Health Sciences; Department of Medicine, Division of Cardiovascular Medicine; Meyers Primary Care Institute

Date

12-5-2012

Document Type

Article

Medical Subject Headings

Atrial Fibrillation; Diagnosis, Computer-Assisted; Algorithms; Heart Rate; Cellular Phone

Disciplines

Analytical, Diagnostic and Therapeutic Techniques and Equipment | Cardiology | Cardiovascular Diseases | Electrical and Computer Engineering

Abstract

BACKGROUND: Atrial fibrillation (AF) is common and associated with adverse health outcomes. Timely detection of AF can be challenging using traditional diagnostic tools. Smartphone use is increasing and may provide an inexpensive and user-friendly means to diagnose AF.

OBJECTIVE: To test the hypothesis that a smartphone-based application could detect an irregular pulse from AF.

METHODS: Seventy-six adults with persistent AF were consented for participation in our study. We obtained pulsatile time series recordings before and after cardioversion using an iPhone 4S camera. A novel smartphone application conducted real-time pulse analysis using 2 statistical methods: root mean square of successive RR difference (RMSSD/mean) and Shannon entropy (ShE). We examined the sensitivity, specificity, and predictive accuracy of both algorithms using the 12-lead electrocardiogram as the gold standard.

RESULTS: RMSDD/mean and ShE were higher in participants in AF than in those with sinus rhythm. The 2 methods were inversely related to AF in regression models adjusting for key factors including heart rate and blood pressure (beta coefficients per SD increment in RMSDD/mean and ShE were-0.20 and-0.35; P

CONCLUSIONS: In a prospectively recruited cohort of 76 participants undergoing cardioversion for AF, we found that a novel algorithm analyzing signals recorded using an iPhone 4S accurately distinguished pulse recordings during AF from sinus rhythm. Data are needed to explore the performance and acceptability of smartphone-based applications for AF detection.

Comments

Citation: Heart Rhythm. 2012 Dec 6. doi: 10.1016/j.hrthm.2012.12.001. [Epub ahead of print] Link to article on publisher's site

Related Resources

Link to Article in PubMed

Keywords

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