Version 1
: Received: 4 December 2023 / Approved: 5 December 2023 / Online: 5 December 2023 (14:23:23 CET)
How to cite:
Boschi, F.; Figini, V.; Pagana, G.; Visintin, M. Optimized Cuffless Blood Pressure Measurement Using ECG, PPG and Linear Regression. Preprints2023, 2023120273. https://doi.org/10.20944/preprints202312.0273.v1
Boschi, F.; Figini, V.; Pagana, G.; Visintin, M. Optimized Cuffless Blood Pressure Measurement Using ECG, PPG and Linear Regression. Preprints 2023, 2023120273. https://doi.org/10.20944/preprints202312.0273.v1
Boschi, F.; Figini, V.; Pagana, G.; Visintin, M. Optimized Cuffless Blood Pressure Measurement Using ECG, PPG and Linear Regression. Preprints2023, 2023120273. https://doi.org/10.20944/preprints202312.0273.v1
APA Style
Boschi, F., Figini, V., Pagana, G., & Visintin, M. (2023). Optimized Cuffless Blood Pressure Measurement Using ECG, PPG and Linear Regression. Preprints. https://doi.org/10.20944/preprints202312.0273.v1
Chicago/Turabian Style
Boschi, F., Guido Pagana and Monica Visintin. 2023 "Optimized Cuffless Blood Pressure Measurement Using ECG, PPG and Linear Regression" Preprints. https://doi.org/10.20944/preprints202312.0273.v1
Abstract
This study investigates the suitability of the timestamp approach algorithm for predicting blood pressure (BP) using wearable and comfortable devices, showcasing its accuracy even with low-frequency sampled signals. Developed within the SINTEC European project, the algorithm utilizes Electrocardiogram (ECG) and Photoplethysmogram (PPG) sensors, demonstrating promising results in both laboratory and hospital settings. The research emphasizes the algorithm’s potential to transform continuous and non-invasive BP monitoring through wearable technology, offering frequent measurements with minimal discomfort. While the study shows promise, further exploration is needed, focusing on optimizing machine learning coefficients for long-term monitoring applications and addressing potential changes over time. Technological improvements, including the implementation of edge computing algorithms, hold the key to enhancing BP accuracy, denoising techniques, and integrating alarm systems for future wearable solutions. This study emphasizes the adaptability of the algorithm for diverse healthcare environments, paving the way for broader applications in convenient and reliable BP monitoring.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
