E.N. Dolzhenko Tubercolotherapist Highest Catehory, Deputy Chief Physician at the Organizational and Methodological Work, Ryazan Regional Clinical Antitubercular Dispensary S.Y. Zhuleva Sinior Lecturer, Department of Computational and Applied Mathematics, Ryazan State Radio Engineering University A.V. Kroshilin Ph.D. (Eng.), Associate Professor, Department of Computational and Applied Mathematics, Ryazan State Radio Engineering University, Professor, Russian Academy of Natural Sciences A.N. Pylkin Dr.Sc. (Eng.), Professor, Corresponding Member of the Russian Academy of Natural Sciences, Honored Worker of Higher School of Russian Federation, Honored Worker of Science and Technology of Russia, Dean of the Faculty of Computer Engineering, Head of the Department of Computational and Applied Mathematics, Ryazan State Radio Engineering University

 The field of magnetic resonance imaging and magnetic therapy is developed rapidly today. But the use of diagnostic equipment together with magnetic equipments is complicated by the fact that the magnetic field, is an obstacle when measuring bioelectric signals. The situation is complicated by the fact that noise has complex structure and high intensity. Thus electrocardiosignal (ECS), which allows to estimatethe patient\'s condition at the time of magnetic therapy, is almost unavailable. Reducing the noise in the receive signals may be possible if its channel penetration has been identifying. The article marked two approaches for solving this problem. Both paths are based on a priori known configuration of the magnetic field in a therapeuticequipment, predetermined by current-time dependencies for each inductor. The first approach is based on obtaining an estimate of the useful signal as an adaptive filtering error, where the input of the adaptive filter is priori known signal of sum equipment inductorscurrent, as the target - the sum of signal noise and the ECS. The second approach is based on the strategy, known as the estimation of receding horizon. The article details the first approach. The structure of the hardware of the electrocardiograph, invariant to the complex jamming environment is described and programming model for complex noise filtering, distorting ECS during the magnetic therapy session is built.

 

1. Kroshilin A.V. Primenenie nechetkojj klasterizacii dlja ehffektivnogo monitoringa statisticheskojj informacii v sistemakh neopredelennosti // Vestnik Rjazanskogo gosudarstvennogo radiotekhnicheskogo universiteta. 2010. № 2 (vyp. 32). S. 71-76.
2. Kroshilin A.V., Vinogradova L.I. Vnedrenie informacionnykh tekhnologijj v Rjazanskom oblastnom klinicheskom protivotuberkuleznom dispansere // Annaly Rjazanskojj ftiziatrii: Sb. nauchno-prakt. rabot / pod red. V.L. Drobina. Rjazan: 2000. S. 33-43.
3. Kroshilin A.V., Cybikova EH.B., Dolzhenko E.N., Vinogradova L.I., Sabgajjda T.P. Ocenka faktorov riska, vlijajushhikh na rezultaty lechenija vpervye vyjavlennykh bolnykh tuberkulezom legkikh // Nauchno-prakticheskijj zhurnal «Tuberkulez i bolezni legkikh». M.: Izd. dom «Nju terra». 2014. № 12.  S. 40-46.
4. Kroshilin A.V., Kroshilina S.V., Pylkin A.N. Postroenie sistem podderzhki prinjatija reshenijj s primeneniem nechetko-mnozhestvennogo podkhoda // Izv. vuzov. Ser. Osnovnye problemy poligrafii i izdatelskogo dela. 2012. № 5. S. 109-117.
5. Kroshilin A.V., Kroshilina S.V., Pylkin A.N. Algoritm modificirovannogo metoda nechetkojj klasterizacii v intellektualnykh medicinskikh sistemakh, Matematicheskie i kompjuternye metody v medicine, biologii i ehkologii: Monografija / pod nauch. red. V.I. Levina. Penza; M.: Privolzhskijj Dom znanijj; MIEHMP. 2013. Vyp. 2. S. 54-65.