Published June 30, 2022 | Version v1
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Development of an atrioventricular block prediction of method for portable heart monitoring system

  • 1. Satbayev University
  • 2. Kazakh National Women's Teacher Training University
  • 3. Institute of Mechanics and Engineering named after U. A. Joldasbekov
  • 4. Zhetysu University

Description

The object of the study is a portable system that allows real-time monitoring of the state of the heart for the timely provision of medical care. The task of detecting atrioventricular (AV) blocks in the conditions of free motor activity of the patient is being solved. To develop a method for detecting AV block, models of the electrical activity of the heart were used to take into account the spatiotemporal organization of the process of spreading excitation, analyze the dynamics of the behavior of the cardiovascular systems (CVS) for any value of the period of atrial excitation, and assess the degree of fitness of the CVS. The proposed method made it possible to determine the heart rate (HR) at which the development of AV block is possible. AV block of the III degree – heart rate 304 bpm; AV block of the II degree with the loss of half of the impulses – heart rate 260 bpm; AV block II degree with loss of individual impulses – heart rate 234 bpm; AV block of the 1st degree – heart rate 200 bpm. Prediction of AV block allows assessing the degree of “training” of the patient’s heart. The obtained quantitative results are consistent with the heart rate values known to modern health care. The developed method was implemented on the basis of a portable ECG monitoring system previously developed by the authors. Tests of the portable ECG monitoring system indicate an increase in the sensitivity and specificity of diagnosing cardiac arrhythmia and confirm the achievement of the goal of this study: improving the efficiency of diagnostics and expanding the functionality of the portable ECG monitoring system

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References

  • Timmis, A., Vardas, P., Townsend, N., Torbica, A., Katus, H., De Smedt, D. et. al. (2022). European Society of Cardiology: cardiovascular disease statistics 2021. European Heart Journal, 43 (8), 716–799. doi: https://doi.org/10.1093/eurheartj/ehab892
  • Opoku-Acheampong, A. A., Rosenkranz, R. R., Adhikari, K., Muturi, N., Logan, C., Kidd, T. (2021). Tools for Assessing Cardiovascular Disease Risk Factors in Underserved Young Adult Populations: A Systematic Review. International Journal of Environmental Research and Public Health, 18 (24), 13305. doi: https://doi.org/10.3390/ijerph182413305
  • Holter, N. J. (1961). New Method for Heart Studies: Continuous electrocardiography of active subjects over long periods is now practical. Science, 134 (3486), 1214–1220. doi: https://doi.org/10.1126/science.134.3486.1214
  • Chou, I.-C., Hsueh, H.-C., Lee, R.-G. (2009). Example for mobile ECG holter design using FMEA model. Biomedical Engineering: Applications, Basis and Communications, 21 (01), 61–70. doi: https://doi.org/10.4015/s101623720900109x
  • Liu, S.-H., Huang, Y.-F., Chen, C.-R. (2014). The wireless holter ECG system based on Zigbee. 2014 IEEE International Conference on Systems, Man, and Cybernetics.
  • Gabbouj, M., Kiranyaz, S., Malik, J., Zahid, M. U., Ince, T., Chowdhury, M. E. H. et. al. (2022). Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 1–12. doi: https://doi.org/10.1109/tnnls.2022.3158867
  • Zbitnieva, V. O., Voloshyna, O. B., Balashova, I. V., Dukova, O. R., Lysyi, I. S. (2021). Incidence of cardiac arrhythmias in patients with COVID-19 infection according to 24-hour electrocardiogram monitoring, Zbitnieva V. O., and etc., Zaporozhye medical journal, 23 (6), 759–765. doi: https://doi.org/10.14739/2310-1210.2021.6.239243
  • Basnet, B. K., Manandhar, K., Shrestha, R., Shrestha, S., Thapa, M. (2009). Electrocardiograph and Chest X-ray in Prediction of Left Ventricular Systolic Dysfunction. Journal of Nepal Medical Association, 48, 176. doi: https://doi.org/10.31729/jnma.343
  • Katscher, U., Weiss, S. (2022). Mapping electric bulk conductivity in the human heart. Magnetic Resonance in Medicine, 87 (3), 1500–1506. doi: https://doi.org/10.1002/mrm.29067
  • Aliev, R. R. (2010). Computer simulation of the electrical activity of the heart. Uspekhi fizicheskikh nauk, 41 (3), 44–63.
  • Noble, D. (1962). A modification of the Hodgkin-Huxley equations applicable to Purkinje fibre action and pacemaker potentials. The Journal of Physiology, 160 (2), 317–352. doi: https://doi.org/10.1113/jphysiol.1962.sp006849
  • Herve, D., Drouard Haelewyn, C., Rousselot, J. F. (2002). The A.B.C. of ECG. Pratique Medicale and Chirurgicale de l'Animal de Compagnie, 37 (6), 487–489.
  • Simova, I., Predovski, M., Christov, I., Simov, D. (2017). Remote ECG Interpretation: Guidelines and their Implementation. 2017 Computing in Cardiology Conference (CinC). doi: https://doi.org/10.22489/cinc.2017.366-095
  • Orlov, V. N. (2017). Rukovodstvo po elektrokardiografii. Moscow: 560. Available at: https://ivgma.ru/attachments/16530
  • Pravdin, S. F., Epanchintsev, T. I., Nezlobinskii, T. V., Panfilov, A. V. (2020). Induced drift of scroll waves in the Aliev–Panfilov model and in an axisymmetric heart left ventricle. Russian Journal of Numerical Analysis and Mathematical Modelling, 35 (5), 273–283. doi: https://doi.org/10.1515/rnam-2020-0023
  • Li, A., Stroik, D., Schaaf, T., Yuen, S., Kleinboehl, E., Cornea, R. L., Thomas, D. D. (2020). Biophysics of the SERCA2A/DWORF Complex, Implications for Treatment of Heart Failure. Biophysical Journal, 118 (3), 593a. doi: https://doi.org/10.1016/j.bpj.2019.11.3210
  • Von Rosenberg, W., Hoting, M.-O., Mandic, D. P. (2019). A physiology based model of heart rate variability. Biomedical Engineering Letters, 9 (4), 425–434. doi: https://doi.org/10.1007/s13534-019-00124-w
  • Stenzinger, R. V., Tragtenberg, M. H. R. (2022). Cardiac reentry modeled by spatiotemporal chaos in a coupled map lattice. The European Physical Journal Special Topics, 231 (5), 847–858. doi: https://doi.org/10.1140/epjs/s11734-022-00473-1
  • Alimbayev, C., Alimbayeva, Z., Ozhikenov, K., Bodin, O., Mukazhanov, Y. (2020). Development of measuring system for determining life-threatening cardiac arrhythmias in a patient's free activity. Eastern-European Journal of Enterprise Technologies, 1 (9 (103)), 12–22. doi: https://doi.org/10.15587/1729-4061.2020.197079