Document Detail


Selection of myocardial electrogram features for use by implantable devices.
MedLine Citation:
PMID:  8258439     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Implantable devices that terminate ventricular tachycardia must be capable of correctly classifying heart rhythms to a high degree of reliability. We evaluated the relative discriminating power of several myocardial electrogram (ME) features in six human subjects by reducing the order of their corresponding feature spaces using three different optimization methods: 1) minimizing univariate Bayes error rates (univariate parametric), 2) maximizing the Kullback divergence (multivariate parametric), and 3) pruning classification trees (nonparametric). We found that although the composition of the optimal subspaces varied considerably from one subject to another, one frequency domain feature was common to most of the optimal subspaces.
Authors:
W J Gibb; D M Auslander; J C Griffin
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  40     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  1993 Aug 
Date Detail:
Created Date:  1994-01-19     Completed Date:  1994-01-19     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  727-35     Citation Subset:  IM    
Affiliation:
Cardiovascular Research Institute, University of California, San Francisco 94143.
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem
Decision Trees
Electrocardiography / instrumentation,  methods*,  statistics & numerical data
Equipment Design
Heart Rate*
Humans
Multivariate Analysis
Pacemaker, Artificial* / statistics & numerical data
Regression Analysis
Sinoatrial Node / physiology
Tachycardia, Atrioventricular Nodal Reentry / diagnosis,  physiopathology,  therapy
Ventricular Function
Grant Support
ID/Acronym/Agency:
HL35008/HL/NHLBI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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