A.N. Kalinichenko1, L.A. Manilo2, A.P. Nemirko3, E.V. Sadykova4, I.V. Degterev5

1–5 St Petersburg Electrotechnical University (LETI) (Saint-Petersburg, Russian)
2 lmanilo@yandex.ru

The paper presents a neural network algorithm for cuffless blood pressure measurement based on computer analysis of the photoplethysmogram signal. An approach is proposed that allows you to significantly reduce the requirements for the size of the training sample required to build an effective machine learning model. The essence of the approach is the procedure for retraining a pre-trained neural network model using a limited amount of training data. At the same time, it is possible to achieve such values of the accuracy of estimating blood pressure values as in full-fledged training, but at the same time, the practical use of the method is significantly simplified.

Kalinichenko A.N., Manilo L.A., Nemirko A.P., Sadykova E.V., Degterev I.V. Improving the accuracy of blood pressure estimation using a photoplethysmogram due to additional training of an artificial neural network. Biomedicine Radioengineering. 2024. V. 27. № 5.
P. 23–28. DOI: https:// doi.org/10.18127/j15604136-202405-03 (In Russian).

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