Your search
In authors or contributors
Towards an efficient prognostic model for fetal state assessment
Resource type
Authors/contributors
- Silva Neto, Manuel Gonçalves da (Author)
- Madeiro, João Paulo do Vale (Author)
- Marques, João Alexandre Lobo (Author)
- Gomes, Danielo G. (Author)
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
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
Academic Units
Link to this record