Devising a method for predicting a blood pressure level based on electrocardiogram and photoplethysmogram signals

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.265066

Keywords:

blood pressure, machine learning, photoplethysmogram, bioelectric signals, pulse wave propagation time

Abstract

Determining the level of blood pressure (BP) in a non-invasive way and without a sphygmomanometer cuff is of great relevance when conducting continuous monitoring or screening studies. In this regard, a method for predicting BP parameters based on the signals of the photoplethysmogram (PPG) and electrocardiogram (ECG) signals has been developed. It is proposed to use, as informative features, the time of pulse wave propagation (PTT) and a set of calculated pulse parameters of PPG. PTT is defined as the time intervals between the R-wave of the ECG and the corresponding characteristic points on the PPG acquired optically from the finger. As parameters of the PPG pulse, the known characteristics of this signal described in the literature are used, as well as additional informative features selected during the study.

In accordance with the above, the tools of machine learning theory were used to construct a classifier model and regression models. The approach described in this paper to determine BP makes it possible to use 10-second ECG signals in any of the 12 common leads and PPG signals from any optical type of sensor.

The built model of the classifier detects three levels of BP: low, normal, and high, at the accuracy metric=0.8494. The regression models predict systolic, diastolic, and mean BP parameters in accordance with the requirements of the British Hypertension Society (BHS) standard by the magnitude of the absolute error.

The proposed method for assessing the level of BP involves real-time measurements and can be used in the design of measuring equipment for screening studies, as well as in continuous monitoring tasks within the framework of BHS requirements.

Author Biographies

Alexey Savostin, Manash Kozybayev North Kazakhstan University

Candidate of Technical Sciences, Professor

Department of Energetic and Radioelectronics

Amandyk Tuleshov, U. Joldasbekov Institute of Mechanics and Engineering

Doctor of Technical Sciences, Professor, CEO, Head of Laboratory

Laboratory of Intelligent Robotic Systems

Kayrat Koshekov, Academy Civil Aviation

Doctor of Technical Sciences, Professor

Department of Science and International Cooperation

Galina Savostina, Manash Kozybayev North Kazakhstan University

Doctor PhD, Associated Professor

Department of Energetic and Radioelectronics

Alexandr Largin, Manash Kozybayev North Kazakhstan University

Doctoral Student

Department of Energetic and Radioelectronics

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Devising a method for predicting a blood pressure level based on electrocardiogram and photoplethysmogram signals

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Published

2022-10-30

How to Cite

Savostin, A., Tuleshov, A., Koshekov, K., Savostina, G., & Largin, A. (2022). Devising a method for predicting a blood pressure level based on electrocardiogram and photoplethysmogram signals. Eastern-European Journal of Enterprise Technologies, 5(2(119), 62–74. https://doi.org/10.15587/1729-4061.2022.265066

Issue

Vol. 5 No. 2(119) (2022): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2022 Alexey Savostin, Amandyk Tuleshov, Kayrat Koshekov, Galina Savostina, Alexandr Largin

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