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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 D.
    Lee, Jinseok
    Maitas, Oscar
    Esa, Nada
    Pidikiti, Rahul
    Carlucci, Alex
    Harrington, Josephine
    Mick, Eric
    Chon, Ki H.
    UMass Chan Affiliations
    Meyers Primary Care Institute
    Department of Medicine, Division of Cardiovascular Medicine
    Department of Quantitative Health Sciences
    Document Type
    Journal Article
    Publication Date
    2013-03-01
    Keywords
    Atrial Fibrillation
    Diagnosis, Computer-Assisted
    Algorithms
    Heart Rate
    Cellular Phone
    UMCCTS funding
    Analytical, Diagnostic and Therapeutic Techniques and Equipment
    Cardiology
    Cardiovascular Diseases
    Electrical and Computer Engineering
    
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    Link to Full Text
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3698570/
    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.
    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.2012.12.001
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/37210
    PubMed ID
    23220686
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    Link to Article in PubMed

    ae974a485f413a2113503eed53cd6c53
    10.1016/j.hrthm.2012.12.001
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