Open AccessWAVELET-CONVERSION IN ELECTROCARDIO SIGNAL PROCESSING
Author(s) -
Vladimir Telezhkin,
Behruz Saidov,
P.А. Ugarov,
A.N. Ragozin
Publication year - 2021
Publication title -
vestnik ûžno-uralʹskogo gosudarstvennogo universiteta. seriâ, kompʹûternye tehnologii, upravlenie, èlektronika
Language(s) - English
Resource type - Journals
eISSN - 2409-6571
pISSN - 1991-976X
DOI - 10.14529/ctcr210107
Subject(s) - wavelet , computer science , wavelet transform , signal processing , signal (programming language) , noise (video) , discrete wavelet transform , second generation wavelet transform , artificial intelligence , pattern recognition (psychology) , wavelet packet decomposition , stationary wavelet transform , speech recognition , computer vision , digital signal processing , computer hardware , image (mathematics) , programming language
In the present work, processing of an electro cardio signal using a wavelet transform is consi-dered. In electrocardiography, various digital signal-processing techniques are used to detect, extract, and analyze the various components of an electrocardiogram. Among them, the wavelet transform technique gives promising results in the analysis of the time-frequency characteristics of the electrocardiogram components. The urgency of solving the problem of improving the quality of life of people with the help of early diagnosis and timely treatment of various cardiac diseases is obvious. The process of automated analysis of a huge database of electrocardiographic data is especially important. Wavelet analysis can be successfully used to smooth and remove noise in the ECG signal. Electrocardiogram signal, cleaned from noise components, looks clearer, while its volume is from 10 to 5% of the original signal, which largely solves the problem of storing cardiac records. Aim. Development of an algorithm for threshold processing of wavelet coefficients and filtering of an electrocardiography signal. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the MATLAB environment that implements continuous and discrete wavelet transform. Results. The work shows the result of filtering the ECG signal with the addition of noise with a signal-to-noise ratio of 35 and 45 dB using the decomposition levels N = 2, N = 3, N = 4. Conclusion. Based on the analysis of the data obtained, it can be concluded that the second level of decomposition is the most optimal for filtering the ECG signal. With an increase in the level of decomposition, the output ratio decreases, at the level N = 4 the output signal-to-noise almost does not exceed the input one, therefore, the filtering becomes ineffective. The correlation coefficient to the fourth level is significantly reduced, which means a significant increase in the distortion introduced by the filtering algorithm.