Document Detail


Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients.
MedLine Citation:
PMID:  17011542     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The aim of this study was to examine whether wavelet transform analysis of the electrocardiogram (ECG) can improve the prediction of the maintenance of sinus rhythm in patients with atrial fibrillation (AF) after external DC cardioversion. We examined a variety of wavelet transform-based statistical markers as potential candidates for the prediction of patient status post-cardioversion. Considering a 'success' as a patient who remains in normal sinus rhythm for one month post cardioversion and 'failure' as a patient who does not, it was shown the proposed non-parametric classification system can achieve 89% specificity at 100% sensitivity using a non-parametric classification method.
Authors:
J N Watson; P S Addison; N Uchaipichat; A S Shah; N R Grubb
Publication Detail:
Type:  Journal Article     Date:  2006-10-02
Journal Detail:
Title:  Computers in biology and medicine     Volume:  37     ISSN:  0010-4825     ISO Abbreviation:  Comput. Biol. Med.     Publication Date:  2007 Apr 
Date Detail:
Created Date:  2007-01-31     Completed Date:  2007-06-29     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1250250     Medline TA:  Comput Biol Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  517-23     Citation Subset:  IM    
Affiliation:
CardioDigital Ltd., Elvingston Science Centre, Gladsmuir, East Lothian, EH33 1EH, Edinburgh, Scotland, UK.
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Atrial Fibrillation / therapy*
Electric Countershock*
Electrocardiography / classification*
Humans
Prognosis
Signal Processing, Computer-Assisted*
Statistics, Nonparametric
Treatment Outcome

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


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