299

To automatically determine the state of the cardiovascular system based on the recorded ECG signals, an artificial neural network is trained to classify signals into various possible states. At the same time, the parameters of heart rate variability (HRV) were extracted from the ECG signals and used as input functions for the neural network. HRV is the fluctuation in the time intervals between adjacent heartbeats. For this, the architecture of a neural network based on a multilayer perceptron and a method for obtaining the necessary parameters in the learning process have been developed, and the classification efficiency has been checked and evaluated.

  • Internet ҳавола
  • DOI
  • UzSCI тизимида яратилган сана 25-03-2021
  • Ўқишлар сони 296
  • Нашр санаси 20-11-2020
  • Мақола тилиIngliz
  • Саҳифалар сони66-72
English

To automatically determine the state of the cardiovascular system based on the recorded ECG signals, an artificial neural network is trained to classify signals into various possible states. At the same time, the parameters of heart rate variability (HRV) were extracted from the ECG signals and used as input functions for the neural network. HRV is the fluctuation in the time intervals between adjacent heartbeats. For this, the architecture of a neural network based on a multilayer perceptron and a method for obtaining the necessary parameters in the learning process have been developed, and the classification efficiency has been checked and evaluated.

№ Муаллифнинг исми Лавозими Ташкилот номи
1 Talatov Y.. илмий ҳодими TDTU
2 Nematov S.Q. professor TDTU
№ Ҳавола номи
1 1. Sameni R. et al. “A nonlinear Bayesian filtering framework for ECG denoising,” IEEE Transactions on Biomedical Engineering. – 2007. – Т. 54. – №. 12. – С. 2172-2185.
2 2. Rangayyan R. M.: John Wiley & Sons, “Biomedical signal analysis,” 2015.
3 3. C. N. Bairey Merz, O. Elboudwarej, and P. Mehta, “The autonomic nervous system and cardiovascular health and disease. A complex balancing act,” JACC: Heart Failure, vol. 3, no. 5, pp. 383-385, 2015.
4 4. Heart rate variability // Kardi.ru. URL:http://www.kardi.ru/ru/index/ Article?Id=37&ViewType=view/.
5 5. Heart rate variability. Measurement standards, physiological interpretation and clinical use Working group of the European Heart Society and the North American Society of Stimulation and Electrophysiology // URL: https://www.incart.ru/assets/pdf/hrv-standards.pdf.
6 6. Luo S., Johnston P. “A review of electrocardiogram filtering,” Journal Electrocardiol, 2010;43(6):486–496.
7 7. G. Moldabek, “Heart rate variability indicators in patients with hypothy¬roidism,” Medical and Health Science Journal, vol. 6, pp. 127-131, 2011.
8 8. F. Shaffer and J. P. Ginsberg, “ An Overview of Heart Rate Variability Metrics and Norms,” Frontiers in Public Health, vol. 5, no. September, pp. 1-17, 2017.
9 9. Agrawal S., Gupta A, “Fractal and EMD based removal of baseline wander and powerline interference from ECG signals,” Comput Bio Med 2013;43(11):1889–1899.
10 10. A. S. Karthiga, M. S. Mary, and M. Yogasini, “Early Prediction of Heart Disease Using Decision Tree Algorithm,” International Journal of Advanced Research in Basic Engineering Sciences and Technology, vol. 3, no. 3, pp. 1-16, 2017.
11 11. W. A. H. Engelse and C. Zeelenberg, “A single scan algorithm for QRS- detection and feature extraction,” Computers in cardiology, vol. 6, no. 1979, pp. 37-42, 1979.
12 12. A. Lourengo, H. Silva, P. Leite, R. Lourengo, and A. L. N. Fred, “Real Time Electrocardiogram Segmentation for Finger based ECG Biometrics.” in Biosignals, 2012, pp. 49-54.
13 13. V. K. Marked, “ Correction of the heart rate variability signal for ectopics and missing beats,” Heart rate variability, 1995.
Кутилмоқда