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ECG Processing

ECG Processing

Lenka Lhotská, Václav Chudácek, Michal Huptych
Copyright: © 2009 |Pages: 24
ISBN13: 9781605662183|ISBN10: 1605662186|ISBN13 Softcover: 9781616926007|EISBN13: 9781605662190
DOI: 10.4018/978-1-60566-218-3.ch007
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MLA

Lhotská, Lenka, et al. "ECG Processing." Data Mining and Medical Knowledge Management: Cases and Applications, edited by Petr Berka, et al., IGI Global, 2009, pp. 137-160. https://doi.org/10.4018/978-1-60566-218-3.ch007

APA

Lhotská, L., Chudácek, V., & Huptych, M. (2009). ECG Processing. In P. Berka, J. Rauch, & D. Zighed (Eds.), Data Mining and Medical Knowledge Management: Cases and Applications (pp. 137-160). IGI Global. https://doi.org/10.4018/978-1-60566-218-3.ch007

Chicago

Lhotská, Lenka, Václav Chudácek, and Michal Huptych. "ECG Processing." In Data Mining and Medical Knowledge Management: Cases and Applications, edited by Petr Berka, Jan Rauch, and Djamel Abdelkader Zighed, 137-160. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-218-3.ch007

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Abstract

This chapter describes methods for preprocessing, analysis, feature extraction, visualization, and classification of electrocardiogram (ECG) signals. First we introduce preprocessing methods, mainly based on the discrete wavelet transform. Then classification methods such as fuzzy rule based decision trees and neural networks are presented. Two examples - visualization and feature extraction from Body Surface Potential Mapping (BSPM) signals and classification of Holter ECGs – illustrate how these methods are used. Visualization is presented in the form of BSPM maps created from multi-channel measurements on the patient’s thorax. Classification involves distinguishing between Holter recordings from premature ventricular complexes and normal ECG beats. Classification results are discussed. Finally the future research opportunities are proposed.

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