Author or contributor

Towards an efficient prognostic model for fetal state assessment

Resource type
Title
Towards an efficient prognostic model for fetal state assessment
Abstract
Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.
Publication
Measurement
Volume
185
Pages
110034
Date
2021-11-01
Journal Abbr
Measurement
Language
en
DOI
10.1016/j.measurement.2021.110034
ISSN
0263-2241
Accessed
9/21/22, 5:01 AM
Library Catalog
ScienceDirect
Extra
1 citations (Crossref) [2022-09-21]
Citation
Silva Neto, M. G. da, Madeiro, J. P. do V., Marques, J. A. L., & Gomes, D. G. (2021). Towards an efficient prognostic model for fetal state assessment. Measurement, 185, 110034. https://doi.org/10.1016/j.measurement.2021.110034