Corrigendum: Determination of aortic characteristic impedance and total arterial compliance from regional pulse wave velocities using machine learning: an in silico study
Vasiliki Bikia
Georgios Rovas
Stamatia Pagoulatou
Nikolaos Stergiopulos- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
by Bikia V, Rovas G, Pagoulatou S and Stergiopulos N (2021). Front. Bioeng. Biotechnol. 9:649866. doi: 10.3389/fbioe.2021.649866
In the published article, there was an error. During proof writing, the reference numbering (namely, (13) as provided by the authors) was not replaced. As a result, the formulas in the section indicated below were followed by “(13)” which does not symbolize anything in the paper.
A correction has been made to Materials and methods, Comparison to Prior Art, Paragraph 1 (bullet points 1 and 2) to remove “(13)”.
This sentence previously stated:
“1. Time-derivative peaks method: Zao = P’max/Q’max (13), where P’max and Q’max are the maximum values of the pressure and flow time derivatives, respectively.
2. Peak flow method: Zao = (PQmax–aDBP)/Qmax (13), where aDBP is the aortic DBP, Qmax is the maximum flow value, and PQmax is the aortic pressure magnitude at the maximum flow value.”
The corrected sentence appears below:
“1. Time-derivative peaks method: Zao = P’max/Q’max, where P’max and Q’max are the maximum values of the pressure and flow time derivatives, respectively.
2. Peak flow method: Zao = (PQmax–aDBP)/Qmax, where aDBP is the aortic DBP, Qmax is the maximum flow value, and PQmax is the aortic pressure magnitude at the maximum flow value.”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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Keywords: non-invasive monitoring, aorta, arterial stiffness, vascular aging, machine learning
Citation: Bikia V, Rovas G, Pagoulatou S and Stergiopulos N (2024) Corrigendum: Determination of aortic characteristic impedance and total arterial compliance from regional pulse wave velocities using machine learning: an in silico study. Front. Bioeng. Biotechnol. 12:1345502. doi: 10.3389/fbioe.2024.1345502
Received: 27 November 2023; Accepted: 21 February 2024;
Published: 08 March 2024.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2024 Bikia, Rovas, Pagoulatou and Stergiopulos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Vasiliki Bikia, vasiliki.bikia@epfl.ch