Abstract
Spatial and frequency analysis are the main approaches to signal classification. The paper investigates statistical algorithms for ECG signal processing, such as correlation function estimation, spectral density (Fourier transform), estimation of R-R intervals distribution, and various variants of wavelet transform. The dynamics of the R-R intervals lengths dispersion variation were also investigated. To distinguish R-R intervals, an ECG approximation was used with the help of a piecewise polynomial continuous and differentiated curve developed by the authors, which has a continuous of first derivative. The algorithm for estimating the first derivative based on proposed splines is developed. The detection of the spline thresholds value for the effective isolation of R peaks in the ECG signal is investigated. Using the spline-approximation derivative allowed us to increase the accuracy of R-R intervals selection up to 6% compared to the known numerical methods. Wavelet transforms were used for the spatial-temporal analysis of the ECG signal, which make it possible to use parts of signals in the systems of operational signal investigation. Also, on the basis of the inverse wavelet transform, the ECG signal was cleared of noise.
These algorithms formed the basis for the creation of a smart system for monitoring and prompt evaluation of cardiac regimens. Combining different approaches to ECG analysis has increased the reliability of the recognition system. Experiments show that the use of a smart system allows us to enough accurately diagnose abnormalities in the heart.
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Pashko, A., Krak, I., Stelia, O., Khorozov, O. (2021). Isolation of Informative Features for the Analysis of QRS Complex in ECG Signals. In: Babichev, S., Lytvynenko, V., Wójcik, W., Vyshemyrskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2020. Advances in Intelligent Systems and Computing, vol 1246. Springer, Cham. https://doi.org/10.1007/978-3-030-54215-3_26
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