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Methods of extracting electrocardiograms from electronic signals and images in the Python environment

100%

Zholmagambetova B. , Mazakov T. , Jomartova S. , Izat A. , Bibalayev O.

tom Vol. 21, No. 3

95--101

High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.

Automated Linearization of a System of Nonlinear Ordinary Differential Equations

100%

Mazakova A. , Jomartova S. , Wójcik W. , Mazakov T. , Ziyatbekova G.

tom Vol. 69, No. 4

655--600

This paper investigates the possibility of automatically linearizing nonlinear models. Constructing a linearised model for a nonlinear system is quite labor-intensive and practically unrealistic when the dimension is greater than 3. Therefore, it is important to automate the process of linearisation of the original nonlinear model. Based on the application of computer algebra, a constructive algorithm for the linearisation of a system of non-linear ordinary differential equations was developed. A software was developed on MatLab. The effectiveness of the proposed algorithm has been demonstrated on applied problems: an unmanned aerial vehicle dynamics model and a twolink robot model. The obtained linearized models were then used to test the stability of the original models. In order to account for possible inaccuracies in the measurements of the technical parameters of the model, an interval linearized model is adopted. For such a model, the procedure for constructing the corresponding interval characteristic polynomial and the corresponding Hurwitz matrix is automated. On the basis of the analysis of the properties of the main minors of the Hurwitz matrix, the stability of the studied system was analyzed.