Performance comparison of machine learning algorithms for the estimation of blood pressure using photoplethysmography

Author:

Nisio Attilio Di,De Palma Luisa,Ragolia Mattia Alessandro,Lanzolla Anna Maria Lucia,Attivissimo Filippo

Publisher

Elsevier BV

Reference82 articles.

1. Machine learning algorithm for non-invasive blood pressure estimation using PPG signals;Zhang;IEEE Fifth Int. Conference on Artificial Intelligence and Knowledge Eng. (AIKE),2022

2. Noninvasive blood pressure classification based on photoplethysmography using K-nearest neighbors algorithm: a feasibility study;Tjahjadi;Information,2020

3. Systolic blood pressure estimation using ECG and PPG in patients undergoing surgery;Shaoxiong;Biomed. Signal Process. Control,2023

4. Analysis of position estimation techniques in a surgical EM tracking system;Attivissimo;IEEE Sens. J.,2021

5. Design and implementation of a photoplethysmography acquisition system with an optimized artificial neural network for accurate blood pressure measurement;Pandey;Microsyst. Technol.,2021

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