Algorithmization of Electrostatic Model for Solving the Direct Problem of Electrography

An analysis of the creation and implementation of algorithms to control cardiological processes occurring in the cardiovascular system using software and hardware diagnostic systems is a required activity. This study considers ways to improve the methods of medical data processing using electronic diagnostic systems, relying on existing methods of mathematical simulation of human body states. This study describes the development of a method that solves the direct problem of electrical cardiography by using complete electrostatic simulations. For this purpose, the selection of key characteristics of simulations of anatomical features and physiology of an organism is used by considering the bioelectrical data obtained experimentally. This study describes the basic relations to characterize the electric field and considers the electrophysical properties of biological tissues. The simulation considered the specifics of the functioning of the proposed method, which imitates the activity of the heart muscle. A novel algorithm is proposed that allows for the detection of myocardial pathological changes that arise during diagnostic procedures, utilizing the reconstructed trajectory of single charge movement. An in-depth study of the electric fields of the controlled object using multipole dispersions was conducted, and the impact of the level of multipole dispersions on the reliability and error of the simulation results was analyzed. The proposed simulation does improve the reliability of the parameters obtained during diagnosis using electronic cardiac systems. The implementation of calculation methods for electrophysiological signals based on this model has the potential to facilitate diagnostic procedures even in the presence of undetected pathologies, offering a valuable complement to traditional diagnostic techniques.

Authors

Publication Details

Journal
Journal of Computer Science
Published
2025-04-01
DOI
https://doi.org/10.3844/jcssp.2025.869.882
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
2.48
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article

Algorithmization of Electrostatic Model for Solving the Direct Problem of Electrography

Т. Г. Асланов, Tatyana Karlova, Naur Z. Ivanov, Shamil Shamil`evich Anudinov
1 citations
Journal of Computer Science
Neural Networks and Applications
2.48
article

Algorithmization of Electrostatic Model for Solving the Direct Problem of Electrography

Т. Г. Асланов, Tatyana Karlova, Naur Z. Ivanov, Shamil Shamil`evich Anudinov
article en
1 citations

Abstract

An analysis of the creation and implementation of algorithms to control cardiological processes occurring in the cardiovascular system using software and hardware diagnostic systems is a required activity. This study considers ways to improve the methods of medical data processing using electronic diagnostic systems, relying on existing methods of mathematical simulation of human body states. This study describes the development of a method that solves the direct problem of electrical cardiography by using complete electrostatic simulations. For this purpose, the selection of key characteristics of simulations of anatomical features and physiology of an organism is used by considering the bioelectrical data obtained experimentally. This study describes the basic relations to characterize the electric field and considers the electrophysical properties of biological tissues. The simulation considered the specifics of the functioning of the proposed method, which imitates the activity of the heart muscle. A novel algorithm is proposed that allows for the detection of myocardial pathological changes that arise during diagnostic procedures, utilizing the reconstructed trajectory of single charge movement. An in-depth study of the electric fields of the controlled object using multipole dispersions was conducted, and the impact of the level of multipole dispersions on the reliability and error of the simulation results was analyzed. The proposed simulation does improve the reliability of the parameters obtained during diagnosis using electronic cardiac systems. The implementation of calculation methods for electrophysiological signals based on this model has the potential to facilitate diagnostic procedures even in the presence of undetected pathologies, offering a valuable complement to traditional diagnostic techniques.

Journal of Computer ScienceVol. 21(4)
Openalex Percentile: Top 10%
Neural Networks and Applications
2.48
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