Arrhythmia Detection and Classification Using Wavelet and ICA

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Author(s): Vahid R. Sabzevari (Azad University of Mashhad, Iran), Asad Azemi (The Pennsylvania State University, USA), Morteza Khademi (Ferdowsi University of Mashhad, Iran), Hossein Gholizade (Ferdowsi University of Mashhad, Iran), Armin Kiani (Ferdowsi University of Mashhad, Iran)and Zeinab S. Dastgheib (Ferdowsi University of Mashhad, Iran)
Copyright: 2008
Pages: 7
Source title: Encyclopedia of Healthcare Information Systems
Source Author(s)/Editor(s): Nilmini Wickramasinghe (Illinois Institute of Technology, USA)and Eliezer Geisler (Illinois Institute of Technology, USA)
DOI: 10.4018/978-1-59904-889-5.ch016

Keywords: Health Information Systems / Medical Information Science Reference / Medicine & Healthcare

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Abstract

The goal of this article is to optimize the feature extraction process by using ICA and wavelet transform, apply the obtained set to several different machine learning schemes, and compare their performances. The article is structured as follows. Section 2.0 describes our proposed method for cardiac arrhythmias detection. Section 3.0 covers an overview of different classifier types that were used in this work. Sections 4.0 and 5.0 summarize our simulation scheme and results. Finally, section 6.0 presents the concluding remarks.

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