Quarterly journal published in SPbPU
and edited by prof. Dmitry Zegzhda
Peter the Great St. Petersburg Polytechnic University
Institute of computer sciences and technologies
information security of computer systems
Information Security Problems. Computer Systems
Published since 1999.
ISSN 2071-8217
DIGITALIZATION AND IDENTIFICATION OF ECG SIGNALS USING WAVELET TECHNOLOGIES

I.A. Sikarev
Russian State Hydrometeorological University
V.Yu. Ivanyuk, V.V. Sakharov
Admiral Makarov State University of Maritime and Inland Shipping

Annotation: A method for identifying signals based on the results of electrocardiogram (ECG) processing performed based on wavelet technologies is considered. The use of digital technologies for processing and diagnostics of ECG signals using wavelet analysis can significantly improve the efficiency and quality of evaluation of pacemaker settings during implantation, as well as in the process of correction of functional modes, diagnostics, in order to eliminate postoperative complications, etc. Digital processing of complex cardiac signals at a qualitatively new level is an indispensable condition for radically improving the processing of the current values of the diagnosed parameters, the widespread use of digital tools for making informed and effective decisions in the field of medical care, as well as for information support of identification processes. A method of approximation is considered and an algorithm for analyzing ECG diagrams obtained during implantation and in the process of choosing the modes of functioning of pacemakers based on the wavelet, transform is given. The presence of high–frequency components and short-term pulses in the spectrum of ECG signals, the evaluation of which is practically impossible by the traditionally used methods of spectral analysis, determined the choice of a method for digitalizing the decomposition of signals into basic frequency rhythms for parametric evaluation of QRS complexes. The approximation method is based on the use of wavelet analysis, which allows deep investigation of such modes. Examples of the use of wavelet analysis for the approximation of ECG diagrams using cubic splines whose interpolation nodes are located on an uneven grid are given. Digital technologies are implemented using the tools of the MATLAB computing environment.
Keywords: electrocardiogram, parametric estimation, identification, wavelet technologies, Dobshy wavelets, cubic spline, signal reconstruction levels, wavelet decomposition coefficients
Pages 82-97