Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/44056
Title: Automatic arrhythmia detection
Authors: Travieso, Carlos M. 
Alonso, Jesús B. 
Ferrer, Miguel A. 
Corsino, Jorge
UNESCO Clasification: 3307 Tecnología electrónica
Issue Date: 2010
Journal: Soft Computing Methods for Practical Environment Solutions: Techniques and Studies
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.
URI: http://hdl.handle.net/10553/44056
ISBN: 9781615208937
DOI: 10.4018/978-1-61520-893-7.ch013
Source: Soft Computing Methods for Practical Environment Solutions: Techniques and Studies, p. 204-218
Appears in Collections:Capítulo de libro
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