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Automatic Arrhythmia Detection

Automatic Arrhythmia Detection

Carlos M. Travieso, Jesús B. Alonso, Miguel A. Ferrer, Jorge Corsino
Copyright: © 2010 |Pages: 15
ISBN13: 9781615208937|ISBN10: 1615208933|ISBN13 Softcover: 9781616923310|EISBN13: 9781615208944
DOI: 10.4018/978-1-61520-893-7.ch013
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MLA

Travieso, Carlos M., et al. "Automatic Arrhythmia Detection." Soft Computing Methods for Practical Environment Solutions: Techniques and Studies, edited by Marcos Gestal Pose and Daniel Rivero Cebrián, IGI Global, 2010, pp. 204-218. https://doi.org/10.4018/978-1-61520-893-7.ch013

APA

Travieso, C. M., Alonso, J. B., Ferrer, M. A., & Corsino, J. (2010). Automatic Arrhythmia Detection. In M. Gestal Pose & D. Rivero Cebrián (Eds.), Soft Computing Methods for Practical Environment Solutions: Techniques and Studies (pp. 204-218). IGI Global. https://doi.org/10.4018/978-1-61520-893-7.ch013

Chicago

Travieso, Carlos M., et al. "Automatic Arrhythmia Detection." In Soft Computing Methods for Practical Environment Solutions: Techniques and Studies, edited by Marcos Gestal Pose and Daniel Rivero Cebrián, 204-218. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-893-7.ch013

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Abstract

In the present chapter, the authors have developed a tool for the automatic arrhythmias detection, based on time-frequency features and using a Support Vector Machines (SVM) as classifier. Arrhythmia Database Massachusetts Institute of Technology (MIT) has been used in the work in order to detect eight different states, seven are pathologies and one is normal. The unions of different blocks and its optimization have found success rates of 99.82% for RR’ interval detection from electrocardiogram (PQRST waves), and 99.23% for pathologic detection. In particular, the authors have used wavelet transform in order to characterize the wave of electrocardiogram (ECG), based on Biorthogonal family, achieving the most discriminative coefficients. A discussion on arrhythmia ECG classification methods is also presented in this paper.

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