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Savostin Alexey Alexandrovich. Born in 1982. Graduated from M. Kozybayev North Kazakhstan State University in 2004 (Petropavlovsk). Passed Ph.D. defense at D. Serikbayev East Kazakhstan State Technical University in 2009 in specialty “Automation and Control of Process and Productions (by Industry)” on topic “Development of an Automated System for Imitation of the Bioelectric Activity of the Heart.” Currently works as Associate Professor at the Department of Energetic and Radioelectronics at M. Kozybayev North Kazakhstan State University. Interested in digital processing of signals and data intellectual analysis. Published 56 papers including 3 monographs, 3 articles in journals indexed in the WoS database, and 1 utility model patent.
Ritter Dmitriy Viktorovich. Born in 1981. Graduated from M. Kozybayev North Kazakhstan State University in 2004 (Petropavlovsk). Passed Ph.D. defense at Omsk State Technical University in 2010, in specialty “05.12.04 Radiotechnics, Including TV Systems and Devices.” Currently works as Associate Professor at the Department of Energetic and Radioelectronics at M. Kozybayev North Kazakhstan State University. Interested in automated design systems and radioelectronic devices. Published 50 papers including 1 article in a journal indexed in the WoS database.
Savostina Galina Vladimirovna. Born in 1985. P Graduated from M. Kozybayev North Kazakhstan State University in 2007 (Petropavlovsk). Defended master’s thesis in 2011. Worked as Senior Teacher at the Department of Energetic and Radioelectronics at M. Kozybayev North Kazakhstan State University. Currently a third-grade PhD student at M. Kozybayev North Kazakhstan State University. Interested in information and communication technologies and data intellectual analysis. Published 19 papers including 2 articles in journals indexed in the WoS database.
Translated by A. Dunaeva
This article presents a new approach to solving the problem of automated detection of myocardial infarction of various localization by electrocardiogram data entries. Only the second standard lead is used in the analysis. The signal in this lead undergoes digital filtering in order to remove low-frequency and high-frequency interference. Then, individual cardio complexes P-QRS-T are extracted from the signal, and the following parameters are calculated for them: minimum value, maximum value, interquartile range, mean absolute deviation, root mean square, mode, and entropy. Using the calculated parameters, a standardized training (learning) dataset is formed. The classifier model represents the k-nearest neighbors algorithm with the Manhattan metric of the distance between the objects and number of neighbors k = 9. After learning, the classifier shows the results by precision pre = 98.60%, by recall rec = 97.34%, by specificity spec = 95.93%, and by accuracy acc = 97.03%. According to the analysis of the obtained results, the suggested classifier model offers certain advantages as compared to existing alternatives.
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- Using the K-Nearest Neighbors Algorithm for Automated Detection of Myocardial Infarction by Electrocardiogram Data Entries
A. A. Savostin
D. V. Ritter
G. V. Savostina
- Pleiades Publishing
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