A.A. Mikheev1, Yu.A. Chelebaeva2, S.V. Chelebaev3

1–3 FSBEI HE Ryazan State Radio Engineering University named after V.F. Utkin (Ryazan, Russia)

1 maa0312@yandex.ru, 2 chel-juliya@yandex.ru, 3 sergeychelr@yandex.ru

Every day, the need for mobile monitoring systems is increasing, detecting heart rate deviations in citizens working or living in hard-to-reach areas. These areas have difficulties with data transmission, so it is advisable to carry out the information processing process at the research site. But, despite the limited hardware costs, it is necessary to ensure high reliability of monitoring results.

Develop a neural network model of the electrocardiogram (ECG) signal conversion subsystem for mobile heart rate monitoring devices based on a recurrent network with an input signal delay circuit, determine the structural diagram of the conversion and segmentation subsystem, train the neural network for ECG signal conversion using multithreading, implement a neural network model of the ECG signal conversion subsystem on programmable logic integrated circuits (FPGA).

A neural network model of the ECG signal conversion subsystem for mobile heart rate monitoring devices based on a recurrent network with an input signal delay circuit has been developed. A structural diagram of the conversion and segmentation subsystem has been defined. The neural network for ECG signal conversion was trained using multithreading. The neural networks of the conversion and segmentation subsystem on FPGAs have been implemented.

The developed neural network model of the ECG signal conversion subsystem can be used in the implementation of mobile heart rate monitoring tools.

Mikheev A.A., Chelebaeva Yu.A., Chelebaev S.V. Development of a neural network model of the electrocardiogram signal conversion subsystem for mobile heart rate monitoring devices // Biomedicine Radioengineering. 2026. V. 29. № 5. Р. 56-62. DOI: https://doi.org/10.18127/j15604136-202605-08

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