RBF Neural Networks for ECG Beat Classification and Arrhythmia Detection

Ali Khazaee

Abstract


This paper describes a method for detection of premature ventricular contractions. The method consists of four stages. First, wavelet denoising is applied for noise reduction of multi-channel high resolution ECG signals. In this stage, the Stationary Wavelet Transform is used. Second, ten ECG morphological features and one timing interval feature are calculated for each different type of ECG beat. 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. Finally, PSO is used to optimize the RBF neural network. A classification accuracy of 100% for training dataset and 96.12% for testing dataset were achieved over seven files from the MIT-BIH arrhythmia database.

Keywords


ECG beat classification, premature ventricular contraction, RBF neural network, PSO

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