Why the study?
Does a classifier based on Discrete Wavelet Transform and Probabilistic Neural Networks improve the classification of ECG signals compared to previous methods?
Does a classifier based on Discrete Wavelet Transform and Probabilistic Neural Networks improve the classification of ECG signals compared to previous methods?
A novel machine learning approach using DWT and PNN offers a fast, low-complexity, and highly accurate method for automated ECG arrhythmia classification.
May support efficient automated ECG arrhythmia classification; hypothesis-generating pending prospective clinical validation.
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.
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Moazzen et al. (2009) studied this question.
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