ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Kopets E. E., Rybin V. G., Vasilchenko O. V., Kurtova K. A., Karimov T. I., Karimov A. I., Butusov D. N. Memristor-based Chaotic Dynamical Model for Generating Electrocardiogram Signal. Izvestiya VUZ. Applied Nonlinear Dynamics, 2025, vol. 33, iss. 5, pp. 691-708. DOI: 10.18500/0869-6632-003176, EDN: UZATFJ

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Language: 
Russian
Heading: 
Applied Problems of Nonlinear Oscillation and Wave Theory
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/0869-6632-003176
EDN: 
UZATFJ

Memristor-based Chaotic Dynamical Model for Generating Electrocardiogram Signal

Autors: 
Kopets Ekaterina Evgenieva, Sankt-Peterburg Electrotechnical University "LETI"
Rybin Vyacheslav Gennadevich, Sankt-Peterburg Electrotechnical University "LETI"
Vasilchenko Oleg Vadimovich, Sankt-Peterburg Electrotechnical University "LETI"
Kurtova Karina Alexandrovna, Sankt-Peterburg Electrotechnical University "LETI"
Karimov Timur Iskandarovich, Sankt-Peterburg Electrotechnical University "LETI"
Karimov Artur Iskandarovich, Sankt-Peterburg Electrotechnical University "LETI"
Butusov Denis Nikolaevich, Sankt-Peterburg Electrotechnical University "LETI"
Abstract: 

The purpose of this study is to create a phenomenological model of the human electrocardiogram based on the McSharry model and to achieve a plausible distribution of interpeak intervals between individual heartbeats.

Methods. This paper presents an advanced approach to synthetic ECG generation using a modified McSharry model. We used chaotic dynamics instead of conventional pseudorandom number generators to better represent the variability in ECG dynamical parameters, such as interpeak intervals. A fourth-order circuit equation with a memristor is introduced as a chaos generator. By adjusting the parameters of this system, one can varythe range of peak parameters in the synthetic ECG. The proposed ECG generator can be implemented as a  computer model or as an analog circuit, depending on the application requirements.

Results. The experimental investigation of generated synthetic signals with time-domain waveforms, phase portraits, and RR tachograms’ analysis demonstrated a good correspondence between the synthetic and real ECGs. It is shown that the modified ECG generation approach provides a reasonably realistic and robust method for simulating synthetic ECG signals.

Conclusion. The reported solution possesses many possible applications such as the calibration of medical cardiographs, medical education, and machine learning models for ECG analysis.
 

Key words: 
memristor
synthetic electrocardiogram
ECG simulation
Nonlinear systems
chaos
Acknowledgments: 
This study was supported by the Russian Science Foundation (RSF), project 23-71-01084.
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Received: 
15.04.2025
Accepted: 
29.04.2025
Available online: 
30.04.2025
Published: 
30.09.2025
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