The main parameters of the electrocardiogram (ECG) were processed on the basis of the neural network apparatus. A decision support algorithm for ECG analysis using a neural network for learning vector quantization is proposed. For the study was chosen such features as the duration of QRS complex, RR interval, amplitude of R-wave and the change in the slope of ST segment and heart rate, which are five inputs to the neural network learning vector quantization. Methods of pre-processing and analysis of extraction of ECG functions based on the ECG database of a medical institution in Matlab are presented. The generalized algorithm of the generated LVQ network and the architecture of the LVQ neural network created using MATLAB are proposed. A method for training a neural network that classifies an ECG signal is presented, based on the training data obtained during the extraction of signs - 5 inputs (R-R interval, R wave amplitude, QRs complex duration, ST segment slope, heart rate), in one of five classes: bradycardia, tachycardia, premature ventricular contraction (PSG) (PVC), myocardial infarction, or in the absence of diseases class.
- Magrupov T.M., Vasil'eva S.A., Magrupova M.T. Analiz i obrabotka mediko-biologicheskoy informacii. Monografiya, Tashkent: TashGTU 2012 .164 s.
- S. Correia, J. Miranda, L. Silva, and A. Barreto, “Labview and Matlab for ECG Acquisition, Filtering and Processing,” 3rd International Conference on Integrity, Reliability and Failure, Porto/Portugal, pp. 20-24, 2009.
- Haque M.F., Ali1 H., Kiber M.A., and Hasan M.T. “Detection of Small Variations of ECG Features Using Wavelet,” ISSN 1819-6608, ARPN Journal of Engineering and Applied Sciences, vol. 4, no. 6, pp 27-30, 2009.
- Atul Sethi, Siddharth Arora, Abhishek Ballaney, Frequency domain analysis of ECG signals using auto-associative neural networks, International Conference on Biomedical and Pharmaceutical Engineering 2006 (ICBPE 2006), pp. 531-536.
- Sasikala P., WahidaBanu Dr. R.S. Extraction of P wave and T wave in electrocardiogram using wavelet transform, International Journal of Computer Science and Information Technologies, Vol.2(1), 2011, pp. 489-493.
- Sayad A.T., Halkarnikar P.P. Diagnosis of heart disease using neural network approach – International Journal of Advances in Science Engineering and Technology. 2014, Vol. 2, Iss. 3, pp. 88-92.
- Ajam N. Heart diseases diagnoses using artificial neural network – Network and Complex Systems. 2015, Vol. 5, No. 4, pp. 7-11.
- Kojuri J., Boostani R., Dehghani P., Nowroozipour F., Saki N. Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram – Journal of Cardiovascular Disease Research. 2015, Vol 6, Iss. 2, pp. 51-60
- Talatov Y., Magrupov T., Radjabov A. A Device for Measuring of Frequency Response Function of Biopotentials. 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). Novosinirsk, Russia, 2019, pp. 0007- 0010.
- Talatov Y., Magrupov T. Algorithmic and Software Analysis and Processing of ECG Signals. 2019 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). Novosinirsk, Russia, 2019, pp. 0403 – 0406.
- Sivanandam. S. N.. Sumathi. S. and Deepa. S. N. “Introduction to Neural Networks using - MATLAB 6.0. learning vector quantization. Р.237 -239.2006. Tata McGraw-Hill.
- https://www.mathworks.com/help/deeplearning/ug/learning-vector-quantization-lvq-neural-networks-1.html [accessed Sept 1 2019].
Magrupov, Talat Madievich; Nеmatov, Sherzod Kalandarovich; and Talatov, Yokubjon Talatovich
"ECG PROCESSING AND ANALYSIS TECHNIQUE BASED ON NEURAL NETWORK LEARNING VECTOR QUANTIZATION,"
Chemical Technology, Control and Management: Vol. 2020
, Article 3.
Available at: https://uzjournals.edu.uz/ijctcm/vol2020/iss4/3