title: PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application.
abstract: BACKGROUND
  Atrial fibrillation (AF) is a common and dangerous rhythm abnormality. Smartphones are increasingly used for mobile health applications by older patients at risk for AF and may be useful for AF screening.
  
  
  OBJECTIVE
  To test whether an enhanced smartphone app for AF detection can discriminate between sinus rhythm (SR), AF, premature atrial contractions (PACs), and premature ventricular contractions (PVCs).
  
  
  METHODS
  We analyzed two hundred and nineteen 2-minute pulse recordings from 121 participants with AF (n = 98), PACs (n = 15), or PVCs (n = 15) using an iPhone 4S. We obtained pulsatile time series recordings in 91 participants after successful cardioversion to sinus rhythm from preexisting AF. The PULSE-SMART app conducted pulse analysis using 3 methods (Root Mean Square of Successive RR Differences; Shannon Entropy; Poincare plot). We examined the sensitivity, specificity, and predictive accuracy of the app for AF, PAC, and PVC discrimination from sinus rhythm using the 12-lead EKG or 3-lead telemetry as the gold standard. We also administered a brief usability questionnaire to a subgroup (n = 65) of app users.
  
  
  RESULTS
  The smartphone-based app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy (0.951) for real-time identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was "useful" and "not complex" to use.
  
  
  CONCLUSIONS
  A smartphone app can accurately discriminate pulse recordings during AF from sinus rhythm, PACs, and PVCs.
authors.affiliation: Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.|Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts, USA.|Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA.
authors.forename: David D
authors.initials: DD
authors.lastname: McMANUS
authors.affiliation: Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts, USA.
authors.forename: Jo Woon
authors.initials: JW
authors.lastname: Chong
authors.affiliation: Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
authors.forename: Apurv
authors.initials: A
authors.lastname: Soni
authors.affiliation: Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.|Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
authors.forename: Jane S
authors.initials: JS
authors.lastname: Saczynski
authors.affiliation: Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
authors.forename: Nada
authors.initials: N
authors.lastname: Esa
authors.affiliation: Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
authors.forename: Craig
authors.initials: C
authors.lastname: Napolitano
authors.affiliation: Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
authors.forename: Chad E
authors.initials: CE
authors.lastname: Darling
authors.affiliation: Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
authors.forename: Edward
authors.initials: E
authors.lastname: Boyer
authors.affiliation: Centers for Behavioral and Preventive Medicine, The Miriam Hospital and Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, Rhode Island, USA.
authors.forename: Rochelle K
authors.initials: RK
authors.lastname: Rosen
authors.affiliation: Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
authors.forename: Kevin C
authors.initials: KC
authors.lastname: Floyd
authors.affiliation: Department of Biomedical Engineering, University of Connecticut, Stores, Connecticut, USA.
authors.forename: Ki H
authors.initials: KH
authors.lastname: Chon
mesh_terms: D000368:Aged; D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D018880:Atrial Premature Complexes / Q000175:diagnosis* / Q000503:physiopathology; D001292:Attitude to Computers; D003937:Diagnosis, Differential; D004562:Electrocardiography; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D063731:Mobile Applications; D017060:Patient Satisfaction; D017156:Photoplethysmography / Q000295:instrumentation*; D011237:Predictive Value of Tests; D011446:Prospective Studies; D011674:Pulse; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D000068997:Smartphone; D011795:Surveys and Questionnaires; D013686:Telemetry / Q000295:instrumentation*; D018879:Ventricular Premature Complexes / Q000175:diagnosis* / Q000503:physiopathology
keywords: atrial fibrillation; detection; premature beats; smartphone; technology