Data-specific signal denoising using wavelets, with applications to ECG data

We discuss performance improvements for a hybrid intelligent signal pattern classifier when denoising techniques are introduced before actually applying the classifier. We show how the efficiency of signal denoising for this classifier depends on several factors: the thresholding techniques chosen, the kind of wavelet used in denoising, the level of wavelets applied, and the synchronization between the wavelet selected and the input data. Results from experiments on ECG data which employ different kinds of wavelets (Haar, Daubechies, Symmlet and Coiflet) show that, for this data, level 3 decomposition using Symmlet wavelets with soft thresholding using the minimax principle gives the best results. We also show that further improvements in performance can be obtained by using vector quantization of wavelet coefficients before thresholding.