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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.

  • Ссылка в интернете
  • DOI
  • Дата создание в систему UzSCI 25-03-2021
  • Количество прочтений 295
  • Дата публикации 20-11-2020
  • Язык статьиIngliz
  • Страницы66-72
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.

№ Имя автора Должность Наименование организации
1 Talatov Y.. илмий ҳодими TDTU
2 Nematov S.Q. professor TDTU
№ Название ссылки
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2 2. Rangayyan R. M.: John Wiley & Sons, “Biomedical signal analysis,” 2015.
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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.
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