DocumentCode :
34304
Title :
Multiscale Adaptive Basis Function Modeling of Spatiotemporal Vectorcardiogram Signals
Author :
Gang Liu ; Hui Yang
Author_Institution :
Dept. of Ind. & Manage. Syst. Eng., Univ. of South Florida, Tampa, FL, USA
Volume :
17
Issue :
2
fYear :
2013
fDate :
Mar-13
Firstpage :
484
Lastpage :
492
Abstract :
Mathematical modeling of cardiac electrical signals facilitates the simulation of realistic cardiac electrical behaviors, the evaluation of algorithms, and the characterization of underlying space-time patterns. However, there are practical issues pertinent to model efficacy, robustness, and generality. This paper presents a multiscale adaptive basis function modeling approach to characterize not only temporal but also spatial behaviors of vectorcardiogram (VCG) signals. Model parameters are adaptively estimated by the “best matching” projections of VCG characteristic waves onto a dictionary of nonlinear basis functions. The model performance is experimentally evaluated with respect to the number of basis functions, different types of basis function (i.e., Gaussian, Mexican hat, customized wavelet, and Hermitian wavelets), and various cardiac conditions, including 80 healthy controls and different myocardial infarctions (i.e., 89 inferior, 77 anterior-septal, 56 inferior-lateral, 47 anterior, and 43 anterior-lateral). Multiway analysis of variance shows that the basis function and the model complexity have significant effects on model performances while cardiac conditions are not significant. The customized wavelet is found to be an optimal basis function for the modeling of spacetime VCG signals. The comparison of QT intervals shows small relative errors (<;5%) between model representations and realworld VCG signals when the model complexity is greater than 10. The proposed model shows great potentials to model space-time cardiac pathological behaviors and can lead to potential benefits in feature extraction, data compression, algorithm evaluation, and disease prognostics.
Keywords :
Gaussian distribution; bioelectric phenomena; cardiology; diseases; medical signal processing; nonlinear functions; space-time adaptive processing; wavelet transforms; Gaussian basis function; Hermitian wavelet; Mexican hat basis function; QT interval comparison; VCG characteristic wave matching projection; algorithm evaluation; anterior myocardial infarction; anterior-lateral myocardial infarction; anterior-septal myocardial infarction; basis function number; basis function type; cardiac condition; cardiac electrical signal; customized wavelet; data compression; disease prognostics; feature extraction; healthy control; inferior myocardial infarction; inferior-lateral myocardial infarction; mathematical modeling; model complexity; model efficacy; model generality; model parameter adaptive estimation; model performance; model representation; model robustness; multiscale adaptive basis function modeling approach; multiway analysis of variance; nonlinear basis function; optimal basis function; realistic cardiac electrical behavior simulation; realworld VCG signal; relative error; space-time cardiac pathological behavior; space-time pattern characterization; spacetime VCG signal modeling; spatiotemporal vectorcardiogram signal; vectorcardiogram signal spatial behavior; vectorcardiogram signal temporal behavior; Adaptation models; Complexity theory; Dictionaries; Electrocardiography; Mathematical model; Modeling; Spatiotemporal phenomena; Basis function modeling; myocardial infarction; vectorcardiogram (VCG); wavelet; Algorithms; Analysis of Variance; Humans; Myocardial Infarction; Reproducibility of Results; Signal Processing, Computer-Assisted; Vectorcardiography;
fLanguage :
English
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
Publisher :
ieee
ISSN :
2168-2194
Type :
jour
DOI :
10.1109/JBHI.2013.2243842
Filename :
6423766
Link To Document :
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