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Comparative Study
. 2013 Mar;10(3):315-9.
doi: 10.1016/j.hrthm.2012.12.001. Epub 2012 Dec 6.

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

David D McManus  1 , Jinseok Lee, Oscar Maitas, Nada Esa, Rahul Pidikiti, Alex Carlucci, Josephine Harrington, Eric Mick, Ki H Chon
Affiliations
Comparative Study

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

David D McManus et al. Heart Rhythm. 2013 Mar.

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 diagnoseAF.

Objective: To test the hypothesis that a smartphone-based application could detect an irregular pulse fromAF.

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<.001). An algorithm combining the 2 statistical methods demonstrated excellent sensitivity (0.962), specificity (0.975), and accuracy (0.968) for beat-to-beat discrimination of an irregular pulse during AF from sinus rhythm.

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.

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Conflict of interest statement

Conflicts of interest: McManus, Lee, and Chon have ownership stake in DxMe, Inc. Dr. Chon has a patent on the algorithm described in the manuscript.

Figures

Figure 1
Figure 1
A prototype of the Pulse Waveform Analysis Application running on an iPhone 4S. From left to right: iPhone 4S camera; fingertip applied to iPhone 4S camera; a representative recording from a patient in atrial fibrillation; a representative recording from a patient in normal sinus rhythm.
Figure 2
Figure 2
A flowchart of the Pulse Waveform Analysis Algorithm
Figure 3
Figure 3
Representative pulse recordings, RR Intervals, and Resultant Statistical Values, obtained using an iPhone 4S from a Patient in Atrial Fibrillation (a, c, e) and Normal Sinus Rhythm (b, d, f).
See this image and copyright information in PMC

Comment in

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