Document Detail

A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection.
MedLine Citation:
PMID:  25201457     Owner:  NLM     Status:  Publisher    
The electrocardiogram (ECG) is a biophysical electric signal generated by the heart muscle, and is one of the major measurements of how well a heart functions. Automatic ECG analysis algorithms usually extract the geometric or frequency-domain features of the ECG signals and have already significantly facilitated automatic ECG-based cardiac disease diagnosis. We propose a novel ECG feature by fitting a given ECG signal with a 20th order polynomial function, defined as PolyECG-S. The PolyECG-S feature is almost identical to the fitted ECG curve, measured by the Akaike information criterion (AIC), and achieved a 94.4% accuracy in detecting the Myocardial Infarction (MI) on the test dataset. Currently ST segment elongation is one of the major ways to detect MI (ST-elevation myocardial infarction, STEMI). However, many ECG signals have weak or even undetectable ST segments. Since PolyECG-S does not rely on the information of ST waves, it can be used as a complementary MI detection algorithm with the STEMI strategy. Overall, our results suggest that the PolyECG-S feature may satisfactorily reconstruct the fitted ECG curve, and is complementary to the existing ECG features for automatic cardiac function analysis.
Bin Liu; Jikui Liu; Guoqing Wang; Kun Huang; Fan Li; Yang Zheng; Youxi Luo; Fengfeng Zhou
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-8-20
Journal Detail:
Title:  Computers in biology and medicine     Volume:  -     ISSN:  1879-0534     ISO Abbreviation:  Comput. Biol. Med.     Publication Date:  2014 Aug 
Date Detail:
Created Date:  2014-9-9     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1250250     Medline TA:  Comput Biol Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2014 Elsevier Ltd. All rights reserved.
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