title: A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation.
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.
authors.affiliation: Cardiac Electrophysiology Section, Cardiovascular Medicine Division, Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01655, USA. mcmanusd@ummhc.org
authors.forename: David D
authors.initials: DD
authors.lastname: McManus
authors.forename: Jinseok
authors.initials: J
authors.lastname: Lee
authors.forename: Oscar
authors.initials: O
authors.lastname: Maitas
authors.forename: Nada
authors.initials: N
authors.lastname: Esa
authors.forename: Rahul
authors.initials: R
authors.lastname: Pidikiti
authors.forename: Alex
authors.initials: A
authors.lastname: Carlucci
authors.forename: Josephine
authors.initials: J
authors.lastname: Harrington
authors.forename: Eric
authors.initials: E
authors.lastname: Mick
authors.forename: Ki H
authors.initials: KH
authors.lastname: Chon
mesh_terms: D000368:Aged; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D040421:Cell Phone; D004867:Equipment Design; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D017156:Photoplethysmography / Q000295:instrumentation*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity