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Automated analysis of spontaneously occurring arrhythmias by implantable devices: Limitations of using rate and timing features alone

dc.contributor.authorCaswell, Stephanie A.en_US
dc.contributor.authorDiCarlo, Lorenzo A.en_US
dc.contributor.authorChiang, Chih-Ming Jamesen_US
dc.contributor.authorJenkins, Janice M.en_US
dc.date.accessioned2006-04-10T18:27:24Z
dc.date.available2006-04-10T18:27:24Z
dc.date.issued1994en_US
dc.identifier.citationCaswell, Stephanie A., DiCarlo, Lorenzo A., Chiang, Chih-Ming J., Jenkins, Janice M. (1994)."Automated analysis of spontaneously occurring arrhythmias by implantable devices: Limitations of using rate and timing features alone." Journal of Electrocardiology 27(Supplement 1): 151-156. <http://hdl.handle.net/2027.42/31898>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6WJ4-4G4X38C-1C/2/0ba981c1d19cb0126d16db6f2396f62een_US
dc.identifier.urihttps://hdl.handle.net/2027.42/31898
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=7884353&dopt=citationen_US
dc.description.abstractReal-time automated systems for arrhythmia analysis by implantable antitachycardia devices have been designed to incorporate two-channel rate criteria with intracavitary atrial and ventricular electrogram morphology. Because the power requirements for morphologic analysis substantially limit antitachycardia device longevity, the authors sought to develop an alternative algorithm that relies solely on rate and three newly developed timing features: onset (median ventricular rate filtering to detect abrupt onset), loss of atrioventricular (AV) sequency (premature ventricular depolarizations), and regularity-multiplicity (minimal median cycle length variation concurrent with integral [n:1] AV periodicity). This system was assessed using spontaneously occurring arrhythmias in patients undergoing electrophysiology studies. Electrograms were captured on FM tape (1-500 Hz) using bipolar catheters in the high right atrium and the left ventricular apex. In 11 patients, 25 distinct arrhythmias were analyzed, which included sinus tachycardia (ST) (1 passage), supraventricular tachycardia (SVT) (6 passages), ventricular tachycardia (VT) with concurrent sinus rhythm (16 passages), VT with concurrent atrial flutter (VT/AFl) (2 passages), and ventricular fibrillation (VF) (1 passage). The algorithm correctly diagnosed 1 of 1 episode of ST, 4 of 6 episodes of SVT, 15 of 16 episodes of VT with concurrent sinus rhythm, 0 of 2 episodes of VT/AFl, and 1 of 1 episode of VF. Ventricular tachycardia episodes were misdiagnosed as SVT because of absence of loss of AV sequency in VT onset (1 episode), presence of multiplicity between VT and AFl (1 episode), and absence of VT regularity during AFl (1 episode). Algorithms that are confined to rate and timing features alone are capable of correctly diagnosing most spontaneously occurring tachyarrhythmias. Misdiagnosis of VT may occur, however, despite the integration of multiple timing features.en_US
dc.format.extent536228 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleAutomated analysis of spontaneously occurring arrhythmias by implantable devices: Limitations of using rate and timing features aloneen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumMedical Computing Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan. USAen_US
dc.contributor.affiliationumMedical Computing Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan. USAen_US
dc.contributor.affiliationumMedical Computing Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan. USAen_US
dc.contributor.affiliationumMedical Computing Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan. USAen_US
dc.identifier.pmid7884353en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/31898/1/0000851.pdfen_US
dc.identifier.sourceJournal of Electrocardiologyen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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