Document Detail

ECG beat detection using a geometrical matching approach.
MedLine Citation:
PMID:  17405371     Owner:  NLM     Status:  MEDLINE    
In the framework of the electrocardiography (ECG) signals, this paper describes an original approach to identify heartbeat morphologies and to detect R-wave events. The proposed approach is based on a "geometrical matching" rule evaluated using a decision function in a local moving-window procedure. The decision function is a normalized measurement of a similarity criterion comparing the windowed input signal with the reference beat-pattern into a nonlinear-curve space. A polynomial expansion model describes the reference pattern. For the curve space, an algebraic-fitting distance is built according to the canonical equation of the unit circle. The geometrical matching approach operates in two stages, i.e., training and detection ones. In the first stage, a learning-method based on genetic algorithms allows us estimating the decision function from training beat-pattern. In the second stage, a level-detection algorithm evaluates the decision function to establish the threshold of similarity between the reference pattern and the input signal. Finally, the findings for the MIT-BIH Arrhythmia Database present about 98% of sensitivity and 99% of positive predictivity for the R-waves detection, using low-order polynomial models.
Kleydis V Suárez; Jesus C Silva; Yannick Berthoumieu; Pedro Gomis; Mohamed Najim
Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  54     ISSN:  0018-9294     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2007 Apr 
Date Detail:
Created Date:  2007-04-04     Completed Date:  2007-04-24     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:  641-50     Citation Subset:  IM    
LAPS, University of Bordeaux I, 351-cours de la Libération, 33405 Talence, France.
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MeSH Terms
Arrhythmias, Cardiac / diagnosis*,  physiopathology*
Artificial Intelligence*
Diagnosis, Computer-Assisted / methods*
Electrocardiography / methods*
Heart Rate*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity

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

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