4.5 Editorial Material

Commentary: Automated detection of preterm condition using uterine electromyography based topological features

Journal

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
Volume 41, Issue 3, Pages 944-945

Publisher

ELSEVIER
DOI: 10.1016/j.bbe.2021.06.001

Keywords

Preterm birth estimation; Oversampling; Electrohysterography

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A recent study proposed a machine learning classifier to detect preterm birth early in pregnancy using a Fourier coefficient envelope. However, the evaluation of the system in the study was flawed, resulting in overly optimistic performance measures being reported.
A recently published study [1] proposed a machine learning classifier to detect preterm birth at an early stage of the pregnancy by making use of an envelope created from Fourier coefficients. The evaluation of this system within this study is, however, fundamentally flawed which resulted in overly optimistic near-perfect predictive performance measures being reported. (C) 2021 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

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