ECG classification as a dynamic process based on a spiking neural network

Authors

  • D.A. Myloserdov Vinnytsia National Technical University
  • O.K. Kolesnytskyj Vinnytsia National Technical University
  • O.S. Volosovych Vinnytsia National Technical University
  • Sholpan Zhumagulova Al-Farabi Kazakh National University City
  • O. O. Korolenko Vinnytsia National Technical University

DOI:

https://doi.org/10.31649/1681-7893-2024-48-2-68-77

Keywords:

Spiking neural network, Electrocardiogram, Arrhythmia, Classification, Time series.

Abstract

The article analyzes the ECG classifying methods. A method of dynamic ECG classification using spiking neural networks is proposed. Dynamic parameters are selected, which are a representation of the ECG signal in a time series. These parameters are fed into the input of a spiking neural network, which outputs both a single heartbeat and a full ECG study. The developed spiking neural network has fast learning and uses large amounts of data for training.

Author Biographies

D.A. Myloserdov, Vinnytsia National Technical University

graduate student of the Department of Computer Sciences  

O.K. Kolesnytskyj, Vinnytsia National Technical University

Ph.D. technical of Sciences, associate professor, professor of the Department of Computer Sciences

O.S. Volosovych, Vinnytsia National Technical University

master's degree, postgraduate student, department of biomedical engineering and opto-electronic systems

Sholpan Zhumagulova, Al-Farabi Kazakh National University City

master's degree, doctoral student of the Department of Artificial Intelligence and Big Data

O. O. Korolenko , Vinnytsia National Technical University

graduate student of the Department of Computer Sciences

References

Janitor O. V., Chuiko G. P., Darnapuk E. S., Kraynyk Ya. M. (2021).Methods of medical signal processing using maple computer mathematics", "Ilion" magazine.

Boashash, B., ed. (2003), Time–Frequency Signal Analysis and Processing: A Comprehensive Reference, Oxford: Elsevier Science, ISBN 978-0-08-044335-5.

Arif M. Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiol Meas. 2008;29(5):555.

Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst. 1987;2(1–3):37–52

Comon P. Independent component analysis, a new concept? Signal Process. 1994;36(3):287–314.

Van The Huy и Duong Tuan Anh. (2016). An efficient implementation of anytime k-medoids clustering for time series under dynamic time warping”. В: Proceedings of the Seventh Symposium on Information and Communication Technology, SoICT 2016, Ho Chi Minh City, Vietnam, December 8-9, 2016..

Bardachenko V.F., Kolesnytskyi O.K., Vasiletskyi S.A. (2003). Prospects for the application of impulse neural networks with a timer representation of information for the recognition of dynamic images. UsiM, No. 6. P. 73-82.

Kolesnytskyj O. K., Bokotsey I. V., Yaremchuk S. S. (2010). Optoelectronic Implementation of Pulsed Neurons and Neural Networks Using Bispin-Devices. Optical Memory & Neural Networks (Information Optics). Vol.19. № 2. Р.154-165.

Ji, Shuiwang; Xu, Wei; Yang, Ming; Yu, Kai (1 січня 2013). 3D Convolutional Neural Networks for Human Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (1): 221–231. ISSN 0162-8828

Guo L, Sim G, Matuszewski B. Inter-patient ECG classification with convolutional and recurrent neural networks. Biocybern Biomed Eng. 2019;39(3):868–79.

Kozemiako V. P., Kolesnytskyj O. K., Lischenko T. S., Wojcik W. and Sulemenov A. (2013). Optoelectronic spiking neural network. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 8698.

Neurocomputer architecture based on spiking neural network and its optoelectronic implementation / Oleh K. Kolesnytskyj; Vladislav V. Kutsman; Krzysztof Skorupski; Mukaddas Arshidinova, Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 1117609 (6 November 2019); doi: 10.1117/12.2536607

MIT-BIH Arrhythmia Database [Електронний ресурс] – Режим доступу: https://physionet.org/content/mitdb/1.0.0/

W. Gerstner, and W. Kistler (2002). Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge: Cambridge University Press, doi:10.1017/CBO9780511815706.

Wójcik Waldemar, Smolarz Andrzej (2017). Information Technology in Medical Diagnostics, July 11, 2017 by CRC Press, 210 Pages.

Highly linear Microelectronic Sensors Signal Converters Based on Push-Pull Amplifier Circuits / edited by Waldemar Wojcik and Sergii Pavlov, Monograph, (2022) NR 181, Lublin, Comitet Inzynierii Srodowiska PAN, 283 Pages. ISBN 978-83-63714-80-2

Pavlov Sergii, Avrunin Oleg, Hrushko Oleksandr, and etc. (2021). System of three-dimensional human face images formation for plastic and reconstructive medicine // Teaching and subjects on bio-medical engineering Approaches and experiences from the BIOART-project Peter Arras and David Luengo (Eds.), Corresponding authors, Peter Arras and David Luengo. Printed by Acco cv, Leuven (Belgium). - 22 P. ISBN: 978-94-641-4245-7.

Pavlov S.V., Avrunin O.G., etc. (2019). Intellectual technologies in medical diagnosis, treatment and rehabilitation: monograph / [S. In edited by S. Pavlov, O. Avrunin. - Vinnytsia: PP "TD "Edelweiss and K", 260 p. ISBN 978-617-7237-59-3.

Romanyuk, O., Zavalniuk, Y., Pavlov, S., etc. (2023). New surface reflectance model with the combination of two cubic functions usage, Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska, , 13(3), pp. 101–10

Kukharchuk, Vasyl V., Sergii V. Pavlov, Volodymyr S. Holodiuk, Valery E. Kryvonosov, Krzysztof Skorupski, Assel Mussabekova, and Gaini Karnakova. (2022). "Information Conversion in Measuring Channels with Optoelectronic Sensors" Sensors 22, no. 1: 271. https://doi.org/10.3390/s22010271.

Vasyl V. Kukharchuk, Sergii V. Pavlov, Samoil Sh. Katsyv, and etc. (2021). Transient analysis in 1st order electrical circuits in violation of commutation laws”, Przegląd elektrotechniczny, ISSN 0033-2097, R. 97 NR 9/2021, p. 26-29, doi:10.15199/48.2021.09.05.

Pavlov S.V, Petruk V.G., Kolesnik P.F. (2007). Photoplethysmohrafic technologies of the cardiovascular control: monography, Vinnitsa: Universum-Vinnitsa, 254 p.

Wójcik W, Mezhiievska I, Pavlov SV, Lewandowski T, Vlasenko OV, Maslovskyi V, Volosovych O, Kobylianska I, Moskovchuk O, Ovcharuk V, et al. (2023). Medical Fuzzy-Expert System for Assessment of the Degree of Anatomical Lesion of Coronary Arteries. International Journal

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Published

2024-11-19

How to Cite

[1]
D. . Myloserdov, O. Kolesnytskyj, O. . Volosovych, S. . Zhumagulova, and O. O. Korolenko, “ECG classification as a dynamic process based on a spiking neural network”, Опт-ел. інф-енерг. техн., vol. 48, no. 2, pp. 68–77, Nov. 2024.

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Section

OptoElectronic/Digital Methods and Systems for Image/Signal Processing

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