Next Article in Journal
Determining the Bulk Parameters of Plasma Electrons from Pitch-Angle Distribution Measurements
Next Article in Special Issue
Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review
Previous Article in Journal
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy
Previous Article in Special Issue
Alterations of Cardiovascular Complexity during Acute Exposure to High Altitude: A Multiscale Entropy Approach
Open AccessArticle

Complexity of Cardiotocographic Signals as A Predictor of Labor

1
Department of Community Medicine, Information and Health Decision Sciences—MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
2
Center for Health Technology and Services Research—CINTESIS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal
3
Department of Obstetrics and Gynecology, Centro Materno-Infantil do Norte-Centro Hospitalar do Porto, 4200-450 Porto, Portugal
4
Instituto de Ciências Biomédicas Abel Salazar, University of Porto, 4200-450 Porto, Portugal
5
Department of Gynecology-Obstetrics and Pediatrics, Faculty of Medicine of University of Porto, 4200-450 Porto, Portugal
6
Centro Hospitalar Universitário de S. João, Alameda Hernâni Monteiro, 4200-101 Porto, Portugal
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(1), 104; https://doi.org/10.3390/e22010104
Received: 14 November 2019 / Revised: 6 January 2020 / Accepted: 13 January 2020 / Published: 16 January 2020
Prediction of labor is of extreme importance in obstetric care to allow for preventive measures, assuring that both baby and mother have the best possible care. In this work, the authors studied how important nonlinear parameters (entropy and compression) can be as labor predictors. Linear features retrieved from the SisPorto system for cardiotocogram analysis and nonlinear measures were used to predict labor in a dataset of 1072 antepartum tracings, at between 30 and 35 weeks of gestation. Two groups were defined: Group A—fetuses whose traces date was less than one or two weeks before labor, and Group B—fetuses whose traces date was at least one or two weeks before labor. Results suggest that, compared with linear features such as decelerations and variability indices, compression improves labor prediction both within one (C-Statistics of 0.728) and two weeks (C-Statistics of 0.704). Moreover, the correlation between compression and long-term variability was significantly different in groups A and B, denoting that compression and heart rate variability look at different information associated with whether the fetus is closer to or further from labor onset. Nonlinear measures, compression in particular, may be useful in improving labor prediction as a complement to other fetal heart rate features. View Full-Text
Keywords: labor; fetal heart rate; entropy; data compression; complexity analysis; nonlinear analysis; preterm labor; fetal heart rate; entropy; data compression; complexity analysis; nonlinear analysis; preterm
Show Figures

Figure 1

MDPI and ACS Style

Monteiro-Santos, J.; Henriques, T.; Nunes, I.; Amorim-Costa, C.; Bernardes, J.; Costa-Santos, C. Complexity of Cardiotocographic Signals as A Predictor of Labor. Entropy 2020, 22, 104. https://doi.org/10.3390/e22010104

AMA Style

Monteiro-Santos J, Henriques T, Nunes I, Amorim-Costa C, Bernardes J, Costa-Santos C. Complexity of Cardiotocographic Signals as A Predictor of Labor. Entropy. 2020; 22(1):104. https://doi.org/10.3390/e22010104

Chicago/Turabian Style

Monteiro-Santos, João; Henriques, Teresa; Nunes, Inês; Amorim-Costa, Célia; Bernardes, João; Costa-Santos, Cristina. 2020. "Complexity of Cardiotocographic Signals as A Predictor of Labor" Entropy 22, no. 1: 104. https://doi.org/10.3390/e22010104

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop