International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 5, No. 3, 8 Mar. 2013
Cover page and Table of Contents: PDF (size: 490KB)
Automated Cardiac Beat Classification Using RBF Neural Networks
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Author(s)
Ali Khazaee
1,*
Index Terms
ECG beat classification, Premature Ventricular Contraction, RBF Neural Network, Wavelet, Genetic Algorithm
Abstract
This paper proposes a four stage, denoising, feature extraction, optimization and classification method for detection of premature ventricular contractions. In the first stage, we investigate the application of wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In this stage, the Stationary Wavelet Transform is used. Feature extraction module extracts ten ECG morphological features and one timing interval feature. Then a number of radial basis function (RBF) neural networks with different value of spread parameter are designed and compared their ability for classification of three different classes of ECG signals. Genetic Algorithm is used to find best value of RBF parameters. A classification accuracy of 100% for training dataset and 95.66% for testing dataset and an overall accuracy of detection of 95.83% were achieved over seven files from the MIT/BIH arrhythmia database.
Cite This Paper
Ali Khazaee, "Automated Cardiac Beat Classification Using RBF Neural Networks", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.3, pp.42-48, 2013. DOI:10.5815/ijmecs.2013.03.06
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