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dc.contributor.authorChong, Jo Woon
dc.contributor.authorEsa, Nada
dc.contributor.authorMcManus, David D.
dc.contributor.authorChon, Ki H.
dc.date2022-08-11T08:08:34.000
dc.date.accessioned2022-08-23T15:59:38Z
dc.date.available2022-08-23T15:59:38Z
dc.date.issued2015-05-01
dc.date.submitted2016-06-15
dc.identifier.citationIEEE J Biomed Health Inform. 2015 May;19(3):815-24. doi: 10.1109/JBHI.2015.2418195. Epub 2015 Mar 31. <a href="http://dx.doi.org/10.1109/JBHI.2015.2418195">Link to article on publisher's site</a>
dc.identifier.issn2168-2194 (Linking)
dc.identifier.doi10.1109/JBHI.2015.2418195
dc.identifier.pmid25838530
dc.identifier.urihttp://hdl.handle.net/20.500.14038/30716
dc.description.abstractWe hypothesize that our smartphone-based arrhythmia discrimination algorithm with data acquisition approach reliably differentiates between normal sinus rhythm (NSR), atrial fibrillation (AF), premature ventricular contractions (PVCs) and premature atrial contraction (PACs) in a diverse group of patients having these common arrhythmias. We combine root mean square of successive RR differences and Shannon entropy with Poincare plot (or turning point ratio method) and pulse rise and fall times to increase the sensitivity of AF discrimination and add new capabilities of PVC and PAC identification. To investigate the capability of the smartphone-based algorithm for arrhythmia discrimination, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion, as well as seven participants with PACs and four with PVCs were recruited. Using a smartphone, we collected 2-min pulsatile time series from each recruited subject. This clinical application results show that the proposed method detects NSR with specificity of 0.9886, and discriminates PVCs and PACs from AF with sensitivities of 0.9684 and 0.9783, respectively.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=25838530&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://dx.doi.org/10.1109/JBHI.2015.2418195
dc.subjectBiomedical Devices and Instrumentation
dc.subjectCardiovascular Diseases
dc.subjectMedical Biotechnology
dc.subjectTelemedicine
dc.titleArrhythmia discrimination using a smart phone
dc.typeJournal Article
dc.source.journaltitleIEEE journal of biomedical and health informatics
dc.source.volume19
dc.source.issue3
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/faculty_pubs/995
dc.identifier.contextkey8734746
html.description.abstract<p>We hypothesize that our smartphone-based arrhythmia discrimination algorithm with data acquisition approach reliably differentiates between normal sinus rhythm (NSR), atrial fibrillation (AF), premature ventricular contractions (PVCs) and premature atrial contraction (PACs) in a diverse group of patients having these common arrhythmias. We combine root mean square of successive RR differences and Shannon entropy with Poincare plot (or turning point ratio method) and pulse rise and fall times to increase the sensitivity of AF discrimination and add new capabilities of PVC and PAC identification. To investigate the capability of the smartphone-based algorithm for arrhythmia discrimination, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion, as well as seven participants with PACs and four with PVCs were recruited. Using a smartphone, we collected 2-min pulsatile time series from each recruited subject. This clinical application results show that the proposed method detects NSR with specificity of 0.9886, and discriminates PVCs and PACs from AF with sensitivities of 0.9684 and 0.9783, respectively.</p>
dc.identifier.submissionpathfaculty_pubs/995
dc.contributor.departmentDepartment of Medicine, Division of Cardiovascular Medicine
dc.source.pages815-24


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