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A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation
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Authors
McManus, David DLee, Jinseok
Maitas, Oscar
Esa, Nada
Pidikiti, Rahul
Carlucci, Alex
Harrington, Josephine
Mick, Eric O.
Chon, Ki H.
UMass Chan Affiliations
Meyers Primary Care InstituteDepartment of Medicine, Division of Cardiovascular Medicine
Department of Quantitative Health Sciences
Document Type
Journal ArticlePublication Date
2013-03-01Keywords
Atrial FibrillationDiagnosis, Computer-Assisted
Algorithms
Heart Rate
Cellular Phone
UMCCTS funding
Analytical, Diagnostic and Therapeutic Techniques and Equipment
Cardiology
Cardiovascular Diseases
Electrical and Computer Engineering
Metadata
Show full item recordAbstract
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.Source
Heart Rhythm. 2013 Mar;10(3):315-9. doi: 10.1016/j.hrthm.2012.12.001. Link to article on publisher's site
DOI
10.1016/j.hrthm.2012.12.001Permanent Link to this Item
http://hdl.handle.net/20.500.14038/37210PubMed ID
23220686Related Resources
ae974a485f413a2113503eed53cd6c53
10.1016/j.hrthm.2012.12.001