Translate this page:
Please select your language to translate the article


You can just close the window to don't translate
Library
Your profile

Back to contents

Software systems and computational methods
Reference:

Zaretskiy A.P., Kuleshov A.P., Gromyko G.A. Application of Gaussian functions to Mathematical Modeling of Endocardial Signals

Abstract: The subject of the research presented by the authors of the article is the mathematical models of endocardial signals from the main electrophysiological parts of the heart with the specified amplitude-time characteristics of information fragments. The authors of the article offer the mechanism for extending the mathematical models in order to generate normal and/or pathological states of the atrioventricular system conducting endocardial electrical impulses. The article contains the results of the comparison of modeled and actual endocardial signals recorded in the course of minimally invasive eletrophysiological examination. These results demonstrate that the designed models are appropriate and applicable for modeling endocardial signals coming from different parts of the heart. The research method used by the authors is the mathematical modeling using Gaussian functions approximating set elements of the endocardial signal coming from different parts of the intracardial space. The main conclusions of the research are the following: - the authors have proved that Guassian functions are applicable for the aforesaid purposes; - they have also described possible modifications of used functions for modeling signals from other endocardial spheres such as the left atrium pulmonary vein entry, mitral aortal zone and other zones  clinical electrophysiologists are particularly interested in; the authors have also demonstrated how the research results can be implemented in the form of the hardware and software complexes using the modern methodologies for assessing efficiency of treating complex heart rhythm disorders. 


Keywords:

radio frequency ablation, asymmetrical function, heart conduction system, electrophysiological study, Gaussian functions, endocardial signals, atrial fibrillation, heart process modeling, atrioventricular conductin


This article can be downloaded freely in PDF format for reading. Download article


References
1. Krueger M, Schmidt V, Tobón C, et al. Modeling Atrial Fiber Orientation in Patient-Specific Geometries: A Semi-automatic Rule-Based Approach. Functional Imaging and Modeling of the Heart; 2011: Springer. p. 223-32.
2. van Oosterom A. Closed-form analytical expressions for the potential fields generated by triangular monolayers with linearly distributed source strength. Med Biol Eng Comput 2012. p. 1-9.
3. D. Du, H. Yang, S. Norring and E. Bennett, "In-Silico Modeling of Glycosylation Modulation Dynamics in hERG Ion Channels and Cardiac Electrical Signals," IEEE Journal of Biomedical and Health Informatics, vol. 18, pp. 205-214, 2014. r. 122.
4. R. D. Simitev and V. N. Biktashev. "Conditions for Propagation and Block of Excitation in an Asymptotic Model of Atrial Tissue," Biophysical Journal. vol. 90, r. 2258-2269.
5. Sameni R, Shamsollahi MB, Jutten C, Babaie-Zade M. Filtering noisy ECG signals using the extended kalman filter based on a modified dynamic ECG model. Computers in Cardiology 2005. r. 1017 – 1020
6. Clifford GD, McSharry PE. Method to filter ecgs and evaluate clinical parameter distortion using realistic ECG model parameter fitting. Computers in Cardiology 2005. r. 715-718.
7. Sahambi JS, Tandon SN, Bhatt RKP. An Automated Approach to Beat by Bea t QT-Interval Analysis. IEEE Engineering in Medicine and Biology 2000. r. 97-101.
8. J. David Burkhardt, L. Di Biase, A. Natale. "Long-Standing Persistent Atrial Fibrillation," Journal of the American College of Cardiology. 60(19), pp1930-1932, 2012. r. 41.
9. January CT, et al. 2014 AHA/ACC/HRS Guideline for the management of patients with atrial fibrillation: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. Circulation, published online March 28, 2014. r. 32.
10. Gromyko G.A., Yashin S.M., Sharikov N.L., Chetverikov S.U., Pasenov G.S., Didenko M.V. Characteristics of coronary artery involvement and probability of appropriate discharges of cardioverter-defibrillator implanted for primary prevention of sudden cardiac death. (2014) Kardiologiya, 54 (3), pp. 4-8.
11. Leif Sörnmo, Martin Stridh, Daniela Husser, Andreas Bollmann, and S Bertil Olsson. Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management. Philos Transact A Math Phys Eng Sci, 367(1887):235–53, Jan 2009.
12. Kukushkin Y.A., Bogomolov A.V., Maistrov A.I. Rhythmocardiogram approximation methods for calculation of spectral parameters of cardiac rhythm variability // Biomedical Engineering. 2010. T. 44. № 3. p. 92-103.
13. O.E. Baksanskiy Kognitivnaya nauka: modelirovanie chelovecheskogo intellekta // Psikhologiya i Psikhotekhnika. - 2010. - 10. - C. 12 - 20.