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.
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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