Document Detail


Artifact processing during exercise testing.
MedLine Citation:
PMID:  10688328     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In signal processing of exercise electrocardiograms (ECGs), artifacts are a recurring problem. It is still difficult to discriminate the ECG curves from artifacts, especially in exercise ECGs and particularly in the high exercise phase. We focused on the artifact problem and worked on two new topics: the Finite Impulse Response Residual Filtering (FRF) algorithm and the Intelligent Lead Switch algorithm. The FRF algorithm reduces the baseline wander and muscle noise in the ECG stream, with much less distortion of the QRS complexes. It subtracts a continuously updated median beat from the current ECG, filters the residual signal with a high-pass and a low-pass filter, and adds the median beat to the filtered residual signal. The Intelligent Lead Switch algorithm takes advantage of the redundancy of a multilead system (eg, standard leads), which is nowadays used during exercise testing. It selects the best leads for QRS detection and thus improves the heart rate calculation, ST segment evaluation, and arrhythmia classification.
Authors:
W Kaiser; M Findeis
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Journal of electrocardiology     Volume:  32 Suppl     ISSN:  0022-0736     ISO Abbreviation:  J Electrocardiol     Publication Date:  1999  
Date Detail:
Created Date:  2000-03-21     Completed Date:  2000-03-21     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0153605     Medline TA:  J Electrocardiol     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  212-9     Citation Subset:  IM    
Affiliation:
GE Marquette Hellige, Freiberg, Germany.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Artifacts
Artificial Intelligence*
Electrocardiography*
Exercise Test*
Expert Systems*
Humans
Sensitivity and Specificity
Signal Processing, Computer-Assisted*

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


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