• RAJESH R. KARHE
• DR.S.N.KALE.

In this work ,we have made an algorithm to classify and detect different types of Electrocardiogram (ECG) signal beats including normal beats(N), premature ventricular contraction (PVC),ventricular tachycardia(VT),super ventricular (SV) and others. Various preprocessing stage are applied to create a new and perfect input vector for the neural classifier. In order to extract feature from electrocardiogram (ECG) signal DWT (direct wavelet transform) is applied. Finally the MIT-BIH database is used to assess the suggestive algorithm. The overall classification accuracy rate is more than 99%..

Electrocardiogram (ECG), Artificial Neural Network (ANN), wavelet Transform

"ECG Analysis Using Wavelet Transform and Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.376-380, June 2019, Available :http://www.jetir.org/papers/JETIR1908211.pdf

"ECG Analysis Using Wavelet Transform and Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp376-380, June 2019, Available at : http://www.jetir.org/papers/JETIR1908211.pdf