TY  - JOUR
T1  - Wavelet Transformation and Wavelet Network Classifier for ECG Classification
AU - Vuppala, Venkata Praveen Kumar AU - Sreeram, Indraneel 
JO  - Asian Journal of Information Technology
VL  - 17
IS  - 2
SP  - 131
EP  - 141
PY  - 2018
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2018.131.141
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2018.131.141
KW  - ECG
KW  -neural
KW  -network
KW  -wavelet
KW  -network
KW  -results
AB  - 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%.
ER  -