Summary
A new approach based on the implementation of probabilistic neural network (PNN) is presented for classification of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. The ECG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and wavelet coefficients were calculated to represent the signals. The aim of the study is classification of the ECG beats by the combination of wavelet coefficients and PNN. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features which well represent the ECG signals and the PNN trained on these features achieved high classification accuracies.
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Extract
Wavelet/Probabilistic Neural Networks for Ecg Beats Classification
1. Introduction
The wavelet transform (WT) can be applied to extract the wavelet coefficients of discrete time signals. This procedure makes use of mult irate signal processing techniques. The proposed scheme is the subband coding or multiresolut ion signal analysis [1, 2]. The multiresolution feature of the WT allows the decomposition of a signal into a number of scales, each scale representing a particular coarseness of the signal under study. The WT provides very general techniques which can be applied to many tasks in signal processing. One very important application is the ability to compute and manipulate data in compressed parameters which are often called features. Thus, the electrocardiogram (ECG) signal, consisting of many data points, can be compressed into a few parameters. These parameters characterize the behavior of the ECG signal. ...See the full content of this document
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