Document Detail


Blind adaptive filtering for non-invasive extraction of the fetal electrocardiogram and its non-stationarities.
MedLine Citation:
PMID:  19143416     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The objective is to extract automatically a beat-to-beat fetal electrocardiogram (fECG) from a maternal electrocardiogram (mECG) using surface electrodes placed on the maternal abdomen and to derive fetal PR, QT, QTc, and QS durations to allow early diagnosis and monitoring treatment of certain fetal cardiac disorders. mECG and abdominal noise in abdominal maternal recordings can be orders of magnitude stronger than the fECG signal and the P and T waves that are embedded in them. A two-stage blind adaptive filtering algorithm was used for fECG extraction, the first stage using frequency-domain electrocardiogram features and the second considering time-domain features. Three channels of abdominal recordings were obtained from 12 patients at 20-40 weeks of gestation. In each case beat-to-beat unaveraged fECGs were isolated. The combined filter allowed identification of diagnostically important PR, QT, and RR durations. Comparison with synthetic data is also included.
Authors:
D Graupe; M H Graupe; Y Zhong; R K Jackson
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Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine     Volume:  222     ISSN:  0954-4119     ISO Abbreviation:  Proc Inst Mech Eng H     Publication Date:  2008 Nov 
Date Detail:
Created Date:  2009-01-15     Completed Date:  2009-02-19     Revised Date:  2009-06-08    
Medline Journal Info:
Nlm Unique ID:  8908934     Medline TA:  Proc Inst Mech Eng H     Country:  England    
Other Details:
Languages:  eng     Pagination:  1221-34     Citation Subset:  IM    
Affiliation:
Department of Electrical and Computer Engineering/Bioengineering, University of Illinois at Chicago, USA graupe@ece.uic.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Artificial Intelligence*
Cardiotocography / methods*
Diagnosis, Computer-Assisted / methods*
Electrocardiography / methods*
Humans
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted*

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


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