The Use of Wavelet Analysis to Denoising of Electrocardiography Signal

The electrocardiography examination, due to its accessibility and simplicity, has an important role in diagnostics of the heart ailments. It enables quick detection of various heart defects, undetectable by other kinds of diagnostic tools, so it is very popular. Nevertheless, the measured signal is exposed to a different disturbances. Among them, the electromagnetic interferences, drift of reference electrode and high frequency noises occurring during the measure, should be included. The frequencies spectrum of the noise overlap the spectrum of the electrocardiography signal, which makes impossible to use a classical filters. In the human’s diagnosis, a high quality of the signal is of a great importance. Therefore, in this paper, an optimal wavelet denoising algorithm for electrocardiography signal is presented. The simulation shows that the use of wavelet analysis during the filtration process allows to remove effectively the noise from the electrocardiography signal, without losing an important information and also improves the quality of the signal. To obtain an unambiguous evaluation of wavelet denoising algorithms the signal-to-noiseratio (SNR), mean square error (MSE), and correlation coefficient were used simultaneously. What is more, a fit coefficient, determining the relation between original and denoised signal, were developed. The best results were achieved with coif5 wavelet basis with 7 decomposition level, heursure thresholding algorithm and sln rescaling function.