Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 61, Issue -, Pages 178-184Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2014.08.010
Keywords
Bioinformatics; Health informatics; Polynomial fitting function; Electrocardiogram; Myocardial infarction; Prediction
Categories
Funding
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB13040400]
- Shenzhen Peacock Plan [KQCX20130628112914301]
- Shenzhen Research Grant [ZDSY20120617113021359]
- China 973 program [2010CB732606]
- MOE Humanities Social Sciences Fund [13YJC790105]
- Doctoral research fund of HBUT [BSQD13050]
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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. (C) 2014 Elsevier Ltd. All rights reserved.
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