Automated analysis of spontaneously occurring arrhythmias by implantable devices: Limitations of using rate and timing features alone
dc.contributor.author | Caswell, Stephanie A. | en_US |
dc.contributor.author | DiCarlo, Lorenzo A. | en_US |
dc.contributor.author | Chiang, Chih-Ming James | en_US |
dc.contributor.author | Jenkins, Janice M. | en_US |
dc.date.accessioned | 2006-04-10T18:27:24Z | |
dc.date.available | 2006-04-10T18:27:24Z | |
dc.date.issued | 1994 | en_US |
dc.identifier.citation | Caswell, 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.uri | http://www.sciencedirect.com/science/article/B6WJ4-4G4X38C-1C/2/0ba981c1d19cb0126d16db6f2396f62e | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/31898 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=7884353&dopt=citation | en_US |
dc.description.abstract | Real-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.extent | 536228 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | en_US |
dc.title | Automated analysis of spontaneously occurring arrhythmias by implantable devices: Limitations of using rate and timing features alone | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Medical Computing Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan. USA | en_US |
dc.contributor.affiliationum | Medical Computing Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan. USA | en_US |
dc.contributor.affiliationum | Medical Computing Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan. USA | en_US |
dc.contributor.affiliationum | Medical Computing Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan. USA | en_US |
dc.identifier.pmid | 7884353 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/31898/1/0000851.pdf | en_US |
dc.identifier.source | Journal of Electrocardiology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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