Arrhythmia Detection Using a Radial Basis Function Network With Wavelet Features

This article describes how the demand of hospital services increasing day by day. The smart service to the patients is highly essential that counts the death rate. The diagnosis of the heart disease facilitates to store our data. It motivates the application of data mining techniques are useful in health sectors. Some progress has been made for data mining in different areas. However, a large gap of this application found in medical and patient services. In this paper authors have taken an approach to detect arrhythmias using wavelet transform and data mining technique. In first stage R-peaks of arrhythmia data has been detected using wavelet transform. In the next stage the wavelet coefficients are consider as the input features to the radial basis function (RBFN) model. It has been found that the peaks have been detected using discrete wavelet transform. However, the result with RBFN using wavelet features outperforms. The accuracy and the mean square error (MSE) are obtained and shown in result section.

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