Enhancing the Classification Accuracy of Cardiac Diseases using Image Denoising Technique from ECG signal

Today, one in ten persons is affected by the cardiac diseases as worldwide. Earlier prediction of these kinds of diseases considered as an important assignment by medical experts. Moreover, many works are available for classifying the heart diseases through the ECG signal analysis. But, only few works are come out with Denoising process before the classification of ECG signals for reduce the unwanted artifact from the ECG signals. This work implements the Baye’s Shrink to remove the noise from the ECG signal images before classification process. The proposed image denoising process also uses the region of interest (ROI) techniques to reduce the computational time over the preprocessing which also improves the classification accuracy by clearly indicating the signal edges.