Decomposition of ECG Signals Using Discrete Wavelet Transform for Wolff Parkinson White Syndrome Patients

Todays biggest problem in front of healthcare professionals is to achieve a highest accuracy while classifying ECG signals. This paper explores diverse possibilities of the decomposition using DWT method in order to classify Wolff Parkinson White Syndrome ECG signals. In this work, ECG signals are discretely sampled till 5th resolution level of decomposition tree using DWT with daubechies wavelet of order 4 (db4), which helps in smoothing the feature more appropriate for detecting changes in signals. The MIT-BIH database were used for some experimental results.