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Asian Journal of Information Technology
Year: 2018 | Volume: 17 | Issue: 2 | Page No.: 131-141
DOI: 10.3923/ajit.2018.131.141  
Wavelet Transformation and Wavelet Network Classifier for ECG Classification
Venkata Praveen Kumar Vuppala and Indraneel Sreeram
Abstract: The Electro Cardio Gram ECG signal is a highly used examination in the field of cardiology these pathologies are generally reflected by disorders of the electrical activity of the heart. In this study, we have addressed the problem of automatic recognition of heart beats through the development and implementation of a method combining wavelet transform with neural networks. This method consists denoising, extraction and classification models for robust automated ECG analysis. For the classification module a hybrid network combining neural networks and wavelets has been proposed, implemented and evaluated for identification of the ECG classes. This technique is based on the use of wavelet functions as activation functions in neural networks which allowed the wavelet network to have a better adaptability and flexibility during the learning process given the parameters of translation and expansion of the functions of wavelets. Indeed, theevaluation of the results obtained by the implemented wavelet network are satisfactory with respect to other neural networks in terms of the rate of classification of the heart beats. The association of neural networks with the wavelet functions made it possible to extract the strengths of the two techniques (the learning capacity of the neural models and the multi resolution analysis of the wavelets). The results obtained showed that the proposed method can be considered as an effective method for classification of cardiac arrhythmias with a very acceptable accuracy of more than 98.78%.
How to cite this article:
Venkata Praveen Kumar Vuppala and Indraneel Sreeram, 2018. Wavelet Transformation and Wavelet Network Classifier for ECG Classification. Asian Journal of Information Technology, 17: 131-141.
DOI: 10.3923/ajit.2018.131.141