title: A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection.
abstract: 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.
authors.affiliation: Cardiovascular Disease Center, First Hospital of Jilin University, Changchun 130021, Jilin, PR China.
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, PR China.
authors.forename: Jikui
authors.initials: J
authors.lastname: Liu
authors.affiliation: Key Laboratory of Zoonosis, Ministry of Education, Norman Bethune College of Medicine, Jilin University, Changchun 130021, Jilin, PR China. Electronic address: qing@jlu.edu.cn.
authors.forename: Guoqing
authors.initials: G
authors.lastname: Wang
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
authors.forename: Kun
authors.initials: K
authors.lastname: Huang
authors.affiliation: Key Laboratory of Zoonosis, Ministry of Education, Norman Bethune College of Medicine, Jilin University, Changchun 130021, Jilin, PR China.
authors.forename: Fan
authors.initials: F
authors.lastname: Li
authors.affiliation: Cardiovascular Disease Center, First Hospital of Jilin University, Changchun 130021, Jilin, PR China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, PR China; School of Science, Hubei University of Technology, Wuhan 430068, PR China.
authors.forename: Youxi
authors.initials: Y
authors.lastname: Luo
authors.affiliation: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, PR China. Electronic address: FengfengZhou@gmail.com.
authors.forename: Fengfeng
authors.initials: F
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000503:physiopathology*
keywords: Bioinformatics; Electrocardiogram; Health informatics; Myocardial infarction; Polynomial fitting function; Prediction