Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Information theoretic measures of perinatal cardiotocography synchronization

1 PeriGen. Inc., Montreal, Canada
2 McGill University, Montreal Canada

Special Issues: Computer Methods and Programs in Prenatal Medicine

We examined the use of bivariate mutual information (MI) and its conditional variant transfer entropy (TE) to address synchronization of perinatal uterine pressure (UP) and fetal heart rate (FHR). We used a nearest-neighbour based Kraskov entropy estimator, suitable to the non-Gaussian distributions of the UP and FHR signals. Moreover, the estimates were robust to noise by use of surrogate data testing. Estimating degree of synchronicity and UP-FHR delay length is useful since they are physiological correlates to fetal hypoxia. Mutual information of the UP-FHR discriminated normal and pathological fetuses early (160 min before delivery) and discriminated normal and metabolic acidotic fetuses slightly later (110 min before delivery), with higher mutual information for progressively pathological classes. The delay in mutual information transfer was also discriminating in the last 50 min of labour. Transfer entropy discriminated normal and pathological cases 110 min before delivery with lower TE values and longer information transfer delays in pathological cases, to our knowledge, the first report of this phenomena in the literature.
  Figure/Table
  Supplementary
  Article Metrics

Keywords biomedical signal processing; cardiotocography; transfer entropy

Citation: Philip A. Warrick, Emily F. Hamilton. Information theoretic measures of perinatal cardiotocography synchronization. Mathematical Biosciences and Engineering, 2020, 17(3): 2179-2192. doi: 10.3934/mbe.2020116

References

  • 1. J. A. Low, R. Victory, E. J. Derrick, Predictive value of electronic fetal monitoring for intrapartum fetal asphyxia with metabolic acidosis, Obstet. Gynecol., 93 (1999), 285-291.
  • 2. P. A. Warrick, E. F. Hamilton, D. Precup, R. Kearney, Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography, IEEE Trans. Biomed. Eng., 57 (2010), 771-779.
  • 3. M. Signorini, G. Magenes, S. Cerutti, D. Arduini, Linear and nonlinear parameters for the analysis of fetal heart rate signal from cardiotocographic recordings, IEEE Trans. Biomed. Eng., 50 (2003), 365-374.
  • 4. F. Marzbanrad, Y. Kimura, M. Palaniswami, A. H. Khandoker, Quantifying the interactions between maternal and fetal heart rates by transfer entropy, PLoS ONE, 10 (2015), e0145672.
  • 5. P. A. Warrick, E. F. Hamilton, Mutual information estimates of CTG synchronization, in Computing in Cardiology, 42 (2015), 137-139.
  • 6. P. A. Warrick, E. F. Hamilton, D. Precup, R. E. Kearney, Identification of the dynamic relationship between intra-partum uterine pressure and fetal heart rate for normal and hypoxic fetuses, IEEE Trans. Biomed. Eng., 56 (2009), 1587-1597.
  • 7. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998.
  • 8. P. Wollstadt, M. Martínez-Zarzuela, R. Vicente, F. J. Díaz-Pernas, M. Wibral, Efficient transfer entropy analysis of non-stationary neural time series, PLOS ONE, 9 (2014), 1-21.
  • 9. J. Jezewski, K. Horoba, A. Matonia, J. Wrobel, Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach, Physiol. Meas., 26 (2005), 753.
  • 10. P. Wollstadt, J. T. Lizier, R. Vicente, C. Finn, M. Martínez-Zarzuela, P. Mediano, et al., IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks, J. Open Source Software, 4 (2018), 1081.
  • 11. A. Kraskov, H. Stögbauer, P. Grassberger, Estimating mutual information, Phys. Rev. E, 69 (2004), 066138,
  • 12. L. Faes, G. Nollo, A. Porta, Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique, Phys. Rev. E, 83 (2011), 051112.
  • 13. T. Schreiber, Measuring information transfer, Phys. Rev. Lett., 85 (2000), 461-464.
  • 14. M. Wibral, N. Pampu, V. Priesemann, F. Siebenhühner, H. Seiwert, M. Lindner, et al., Measuring information-transfer delays, PLoS ONE, 8 (2013), e55809.
  • 15. M. Wibral, J. T. Lizier, S. Vögler, V. Priesemann, R. Galuske, Local active information storage as a tool to understand distributed neural information processing, Front. Neuroinf., 8 (2014), 1.

 

Reader Comments

your name: *   your email: *  

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved