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Malignant Ventricular Ectopy Classification using Wavelet Transformation and Probabilistic Neural Network Classifier
Objective: The objective of this paper is to make a distinction between malignant ventricular Ectopic ElectrocarDiogram (ECG) signals from normal ones. Methods: The dataset is taken from MIT-BIH Physio bank ATM. The feature extraction has been done using the Discrete Wavelet Transformation (DWT) method. The experimental ECG signals have been decomposed till 5th level of resolution using daubechies wavelet of order 4 followed by computing various values. Based on the values, classification is performed using Probabilistic Neural Network (PNN) concept. Findings: This paper gives an independent approach for classifying malignant ventricular ectopy (MVE) ECG signals helping health care professionals. Application: The proposed method has been analyzed to be very effective in the classification of MVE ECG signals.
Discrete Wavelet Transformation, Electrocardiograph, Malignant Ventricular Ectopic Beats, MIT-BIH Database, Probabilistic Neural Network.
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