Corrigendum: Estimation of left ventricular end-systolic elastance from brachial pressure waveform via deep learning
Vasiliki Bikia* 
Marija Lazaroska 
Deborah Scherrer Ma Méline Zhao 
Georgios Rovas 
Stamatia Pagoulatou 
Nikolaos Stergiopulos- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
A Corrigendum
Estimation of left ventricular end-systolic elastance from brachial pressure waveform via deep learning
by Bikia V, Lazaroska M, Scherrer Ma D, Zhao M, Rovas G, Pagoulatou S and Stergiopulos N (2021). Front. Bioeng. Biotechnol. 9:754003. doi: 10.3389/fbioe.2021.754003
In the published article, there was an error. Wrong numbers have been used for the data train/validation/test split.
A correction has been made to the section Material and Methods, Data Pre-processing, Paragraph 1. This sentence previously stated:
“The train/validation/test split was set to be 60% (2,290 cases)/20% (764 cases)/20% (764 cases). By computing the MSE with decreasing training size, we noticed that similar results can also be achieved with fewer samples (e.g., 1,603) and, therefore, we may deduce that a training size of 2,290 is sufficient.”
The corrected sentence appears below:
“The train/validation/test split was set to be 60% (2,248 cases)/20% (750 cases)/20% (750 cases). By computing the MSE with decreasing training size, we noticed that similar results can be achieved with fewer samples (e.g., 1,603) and, therefore, we may deduce that a training size of 2,248 is sufficient.”
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.
Publisher’s note
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Keywords: cardiac monitoring, convolution neural networks, cardiovascular modelling, non-invasive, contractility
Citation: Bikia V, Lazaroska M, Scherrer Ma D, Zhao M, Rovas G, Pagoulatou S and Stergiopulos N (2023) Corrigendum: Estimation of left ventricular end-systolic elastance from brachial pressure waveform via deep learning. Front. Bioeng. Biotechnol. 11:1341852. doi: 10.3389/fbioe.2023.1341852
Received: 21 November 2023; Accepted: 28 November 2023;
Published: 05 December 2023.
Edited and reviewed by:
Yih-Kuen Jan, University of Illinois at Urbana–Champaign, United StatesCopyright © 2023 Bikia, Lazaroska, Scherrer Ma, Zhao, 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, vickybikia@gmail.com