K.V. Zaichenko - Doctor of Science (Technology), professor, head of the department of medical radioelectronics of St. Petersburg State University of aerospace instrumentation. E-mail: kvz_41@aanet.ru
N.A. Gorelova - senior lecturer of medical electronics department, St. Petersburg State University of Aerospace Instrumentation (SUAI)
V.P. Omelchenko - Doctor of .Science (biology), professor, head of Department of Medical and Biological Physics, Rostov State Medical University
P.G. Polivanniy - Master of Engineering and Technology «Applied Computer Science». St. Petersburg State University of Aerospace Instrumentation (SUAI)

The most important task in the analysis of high-resolution ECG is to develop a set of methods for secondary treatment, including a synthesis of classical methods, which are widely used in radar in the problems of detection and identification signals, methods of adaptation and learning pattern recognition systems - pathological patterns, indicating the occurrence of ischemia. To improve the accuracy of the timing of the analyzed complex quasi-periodic bioelectric signals were developed procedures, algorithms and program of research and evaluation of high-precision time position of the characteristic points ECG signal. Were synthesized by a variety of high-precision synchronization algorithms mnogootschetnye pacemaker. We propose a method for temporal scalability with the calculation of the optimal values of the average cycle time, providing minimal distortion of the results analysis. Application of the method of quasi-periodic time scale of bioelectric signals, allowed a simultaneous accumulation throughout the cardiac cycle and three-dimensional mapping, which allows to judge the dynamics of the individual elements of the cardiac cycle throughout the analyzed signal. A study of the applicability of the wavelet transform and statistical analysis of data to identify hidden signs of developing cardiac events. The first results of the wavelet processing electrocardiograms experimental animals with artificially induced ischemic heart disease (IHD). Of the signal sampling was done to observe changes in the spectrum throughout the RR-interval in the course of development of IHD. On the spectrograms and bar charts displaying good change of frequency components in the development of ischemic stroke. On the basis of these and other statistical studies of ultra-high resolution ECG signal plans to create a secondary database that is designed based on the results of diagnostics for extending the set of attributes with the possibility of adaptation and learning. For further analysis of factors affecting the appearance in this frequency range, the corresponding components and pathological patterns, you will need to use the method of weighting coefficients. Later, with the assistance of data mining procedures will set up an expert system that will allow biophysicists to clarify the nature and explore in more detail the statistical properties of micropotential wells in the electrical activity of the cells, tissues and organs, to detect and identify new diagnostic signs of disease.

 

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