ECG Signals Classification Based on Wavelet Transform and Probabilistic Neural Networks

In this paper a very intelligent tool with low computational complexity is presented for Electroencephalogram (ECG) signal classification. The proposed classifier is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Network (PNN). The novelty of this approach is that signal statistics, morphological analysis and DWT of the histogram of signal (density estimation) altogether have been used to achieve a higher recognition rate. ECG signals and their density estimation are decomposed into sub-classes using DWT. A PNN is used to classify ECG signals using statistical discriminating features which are extracted from ECG and its sub-classes. Experimental results on five classes of ECG signals from MIT-BIH arrhythmia database show that the proposed method learns very fast, low computational complexity, and a very high performance compared to the previous methods.