Arrhythmia Detection and Classification Using Wavelet and ICA

Arrhythmia Detection and Classification Using Wavelet and ICA

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
DOI: 10.4018/978-1-59904-889-5.ch016
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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.

Key Terms in this Chapter

K-Nearest Neighbor Algorithm (K-NN): A method for classifying objects based on closest training examples in the feature space.

Wavelet Transform: Representation of a signal in terms of a finite length or fast decaying oscillating waveform (known as the mother wavelet). This waveform is scaled and translated to match the input signal.

Cardiac Arrhythmia: A group of conditions in which the muscle contraction of the heart is irregular, or is faster or slower than normal.

Multilayer Perceptron: A class of networks consists of multiple layers of computational units, usually interconnected in a feed-forward way.

Independent Component Analysis: A computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non-Gaussian source signals.

Artificial Neural Network (ANN): An interconnected group of artificial neurons that uses a mathematical model, or computational model, for information processing, based on a connectionist approach to computation.

Radial Basis Function (RBF): A real-valued function whose value depends only on the distance from the origin. In artificial neural networks, radial basis functions are utilized as activation functions.

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