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ANALYSIS OF BIOSIGNALS USING A MODEL OF CYCLIC RHYTHMICALLY RELATED RANDOM PROCESSES
Abstract
The paper suggests the use of diagnostic features in the form of decomposition coefficients of biosignals statistical estimates in the normal range and in different types of pathologies obtained on the basis of a mathematical model in the form of a cyclic random process. As a criterion for choosing the necessary spectral coefficients of the biosignal decompositions in the Fourier basis set, was chosen the energy criterion. As diagnostic features, was used a such spectral coefficients that according to the Bessel inequality, contribute to the energy of the cardiosignal statistical estimation realization not less than 95% at their minimal number. In the work a software complex was developed for the processing and modeling of synchronously registered cardiosignals using models and methods of the theory of cyclic functional relationship. In general, the process of complex analysis of the electrocardiogram on the basis of the model in the form of a cyclic random process includes the following stages: 1) Segmentation; 2) Rhythm function evaluation; 3) Statistical processing ; 4) Statistical estimation normalization; 5) Statistical estimates decompositions. The complex analysis described above can be used in cardiac diagnostic systems in order to detect pathologies associated with changing the shape of the cardiac signal itself, which will serve as diagnostic information for the physician.
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