De-noising the ECG Signal Using DWT and Kernel Adaptive Filter

The use of electrocardiogram (ECG) plays significant role in the diagnosis of heart disease and human computer interface etc. But, the ECG signals get affected from different types of noise during data acquisition due to which it faces the problem to detect actual abnormality. De-noising of the ECG signal is so indispensable and for de-noising it various researcher work in this area. In this work, we propose an approach which uses DWT and together with kernel adaptive intensity transfer function extract the signal. The original ECG single is taken from MIT-BIH arrhythmia database is corrupted with dissimilar types of noise and is used for the analysis. The experimental analysis of the proposed approach is done in MATLAB using the performance measuring parameter such as MSE, PSNR and PRD. The simulation outcomes of the proposed gives improved results than the existing approach.