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

Currently, there is a great need for mobile monitoring systems used to detect heart rate abnormalities in citizens working or living in hard-to-reach areas. Due to problems with transmitting data from such areas, it is desirable to carry out the entire information processing process at the study site. But, despite the limited hardware costs for the implementation of such systems, it is necessary to ensure high reliability of monitoring results.

Purpose is to develop a structural diagram of a mobile heart rate analysis device, select the architecture of the neural network of the conversion and segmentation subsystem of the mobile device, determine the structure of the neural network of the conversion and segmentation subsystem, train the neural network of the conversion and segmentation subsystem, conduct software modeling of the neural network of the conversion and segmentation subsystem, implementation of a neural network conversion and segmentation subsystem based on field programmable gate arrays (FPGAs).

A structural diagram of a mobile heart rate analysis device has been developed. The choice of the neural network architecture of the mobile device conversion and segmentation subsystem is justified. The structure of the neural network of the transformation and segmentation subsystem is determined. The neural network of the transformation and segmentation subsystem was trained. A software simulation of the neural network of the transformation and segmentation subsystem was carried out, confirming the high reliability of the proposed neural network structure. A neural network conversion and segmentation subsystem has been implemented in the hardware description language VHDL based on an FPGA.

The developed structural diagram of the mobile device and the neural network structure of the conversion and segmentation subsystem can be used when implementing mobile heart rate monitoring means.

Mikheev A.A., Chelebaeva Yu.A., Chelebaev S.V. Development of mobile heart rate monitoring tools using artificial neural networks. Biomedicine Radioengineering. 2024. V. 27. № 4. Р. 72-79. DOI: https://doi.org/10.18127/j15604136-202404-10 (In Russian).

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