Skip to main content
SpringerLink
Log in

An exploratory approach to fetal heart rate–pH-based systems

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

This paper presents an exploratory approach of the fetal heart rate (FHR) analysis, aiming to highlight potential limitations of the current predictive modeling attempts. To do so, a set of features that are usually encountered in FHR analysis as well as features extracted using a variant of symbolic aggregate approximation were projected onto a lower-dimensional space where patterns can easily be discerned. The results show, both in a qualitative and a quantitative manner, that there is high overlap between the classes that are formed using solely the umbilical cord pH information, irrespective of the selected dimensionality reduction method. These findings suggest that there is probably a limit to the performance expectation of the current pH-based systems and that alternative approaches should be also pursued to enhance the utility of computer-based decision support technologies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. In two and three dimensions, the human eye is an excellent pattern recognition tool and easily perceives the complexity of a potential classification task and can also discern the existence of structures/patterns in the data. However, the use of three-dimensional (3D) scatter plots was not exploited, to avoid the extra complexity imposed by the selection of the best viewpoint.

References

  1. Chen, H.Y., Chauhan, S.P., Ananth, C.V., Vintzileos, A.M., Abuhamad, A.Z.: Electronic fetal heart rate monitoring and its relationship to neonatal and infant mortality in the United States. Am. J. Obstet. Gynecol. 204, 491.e1–491.e10 (2011)

    Article  Google Scholar 

  2. Blackwell, S.C., Grobman, W.A., Antoniewicz, L., Hutchinson, M., Bannerman, C.G.: Interobserver and intraobserver reliability of the NICHD 3-tier fetal heart rate interpretation system. Am. J. Obstet. Gynecol. 205(4), 378.e1–378.e5 (2011)

    Article  Google Scholar 

  3. Alfirevic, Z., Devane, D., Gyte, G.M.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database Syst. Rev. 3, CD006066 (2006)

    Google Scholar 

  4. FIGO: Guidelines for the use of fetal monitoring. Int. J. Gynaecol. Obstet. 25, 159–167 (1986)

    Google Scholar 

  5. Balayla, J., Shrem, G.: Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis. Arch. Gynecol. Obstet. 300, 7–14 (2019)

    Article  Google Scholar 

  6. Dash, S., Quirk, J.G., Djurić, P.M.: Fetal heart rate classification using generative models. IEEE Trans. Biomed. Eng. 61(11), 2796–2805 (2014)

    Article  Google Scholar 

  7. Petrozziello, A., Jordanov, I., Papageorghiou, A., Redman, W.G., Georgieva, A.: Deep learning for continuous electronic fetal monitoring in labor. In: Conference on Proceedings IEEE Engineering in Medical and Biology Society, pp. 5866–5869 (2018)

  8. Feng, G., Quirck, J, Djuric, P.: Supervised and unsupervised learning of fetal heart rate tracings with deep Gaussian processes. In: Presented at 14th Symposium on Neural Networks and Applications (NEUREL) (2018)

  9. Czabanski, R., Jezewski, M., Wrobel, J., Jezewski, J., Horoba, K.: Predicting the risk of low-fetal birth weight from cardiotocographic signals using ANBLIR system with deterministic annealing and epsilon-insensitive learning. IEEE Trans. Inf Technol. Biomed. 14(4), 1062–1074 (2010)

    Article  Google Scholar 

  10. Georgoulas, G., Stylios, C.D., Groumpos, P.P.: Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines. IEEE Trans. Biomed. Eng. 53(5), 875–884 (2006)

    Article  Google Scholar 

  11. Spilka, J., Chudáček, V., Koucký, M., Lhotská, L., Huptych, M., Janků, P., Georgoulas, G., Stylios, C.: Using nonlinear features for fetal heart rate classification. Biomed. Signal Process. Control 7(4), 350–357 (2012)

    Article  Google Scholar 

  12. Fanelli, A., Magenes, G., Campanile, M., Signorini, M.G.: Quantitative assessment of fetal well-being through CTG recordings: a new parameter based on phase-rectified signal average. IEEE J. Biomed. Health Inform. 17(5), 959–966 (2013)

    Article  Google Scholar 

  13. Georgoulas, G., Karvelis, P., Spilka, J., Chudáček, V., Stylios, C.D., Lhotská, L.: Investigating pH based evaluation of fetal heart rate (FHR) recordings. Health Technol. 7, 1–14 (2017)

    Article  Google Scholar 

  14. Spilka, J., Frecon, J., Leonarduzzi, R., Pustelnik, N., Abry, P., Doret, M.: Sparse support vector machine for intrapartum fetal heart rate classification. IEEE J. Biomed. Health Inform. 21(3), 664–671 (2017)

    Article  Google Scholar 

  15. Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.: Artificial neural networks applied to fetal monitoring in labour. Neural Comput. Appl. 22(1), 85–93 (2013)

    Article  Google Scholar 

  16. Czabański, R., Jeżewski, J., Horoba, K., Jeżewski, M.: Fetal state assessment using fuzzy analysis of fetal heart rate signals—agreement with the neonatal outcome. Biocybern. Biomed. Eng. 33(3), 145–155 (2013)

    Article  Google Scholar 

  17. Ocak, H., Ertunc, H.M.: Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems. Neural Comput. Appl. 23(6), 1583–1589 (2013)

    Article  Google Scholar 

  18. Dash, S., Quirk, J.G., Djuric, P.M.: Learning dependencies among fetal heart rate features using Bayesian networks. In: Engineering in Medicine and Biology Society (EMBC), pp. 6204–6207 (2012)

  19. Norén, H., Amer-Wåhlin, I., Hagberg, H., Herbst, A., Kjellmer, I.: Fetal electrocardiography in labor and neonatal outcome: data from the Swedish randomized controlled trial on intrapartum fetal monitoring. Am. J. Obstet. Gynecol. 188(1), 183–192 (2003)

    Article  Google Scholar 

  20. Spilka, J.: Complex approach to fetal heart rate analysis: a hierarchical classification model. Ph.D. Thesis, Czech Technical University in Prague Department of Cybernetics (2013)

  21. Costa Santos, C., Bernardes, J., Vitányi P.M., Antunes, L.: Clustering fetal heart rate tracings by compression. In: Computer-Based Medical Systems, CBMS, pp. 685–690 (2006)

  22. Ferrario, M., Signorini, M.G., Magenes, G.: Complexity analysis of the fetal heart rate variability: early identification of severe intrauterine growth-restricted fetuses. Med. Biol. Eng. Comput. 47(9), 911–919 (2009)

    Article  Google Scholar 

  23. Chudacek, V., Spilka, J., Bursa, M., Janku, P., Hruban, L., Huptych, M., Lhotska, L.: Open access intrapartum CTG database. BMC Pregnancy Childbirth 14, 16 (2014)

    Article  Google Scholar 

  24. Lin, J., Keogh, E., Lonardi S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003)

  25. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, San Diego (2009)

    MATH  Google Scholar 

  26. Wasikowski, M., Chen, X.: Combating the small sample class imbalance problem using feature selection. IEEE Trans. Knowl. Data Eng. 22(10), 1388–1400 (2010)

    Article  Google Scholar 

  27. Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007)

    Article  MathSciNet  Google Scholar 

  28. Yousofvand, L., Abdolhossein, F., Fardin, A.: Person identification using ECG signal’s symbolic representation and dynamic time warping adaptation. Signal Image Video Process. 13(2), 245–251 (2019)

    Article  Google Scholar 

  29. Keogh, E., Lin J., Fu., A.: Hot sax: efficiently finding the most unusual time series subsequence. In: 5th IEEE International Conference on Data Mining, pp. 226–233 (2005)

  30. Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. J. Knowl. Inf. Syst. 3(3), 263–286 (2000)

    Article  Google Scholar 

  31. Yi, K., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. In: 26th International Conference on Very Large Databases, Cairo, Egypt, (2000)

  32. Cox, T.F., Cox, M.A.: Multidimensional Scaling. CRC Press, Boca Raton (2010)

    MATH  Google Scholar 

  33. van der Maaten, L.J., Postma, E.O., van den Herik, H.J.: Dimensionality reduction: a comparative review. J. Mach. Learn. Res. 10, 66–71 (2009)

    Google Scholar 

  34. Machado, J.A., Tenreiro, E., Dumitru, B.: Analysis of UV spectral bands using multidimensional scaling. Signal Image Video Process. 9(3), 573–580 (2015)

    Article  Google Scholar 

  35. Lerner, B., Guterman, H., Aladjem, M., Dinsteint, I., Romem, Y.: On pattern classification with Sammon’s nonlinear mapping an experimental study. Pattern Recognit. 31(4), 371–381 (1998)

    Article  Google Scholar 

  36. Preben, F., Moeslund, T.: Invariant gait continuum based on the duty-factor. Signal Image Video Process. 3(4), 391 (2009)

    Article  Google Scholar 

  37. Sammon, J.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 18(5), 401–409 (1969)

    Article  Google Scholar 

  38. Tenenbaum, J., De Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  39. Demartines, P., Hérault, J.: Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets. IEEE Trans. Neural Netw. 8(1), 148–154 (1997)

    Article  Google Scholar 

  40. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  41. Suykens, J., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999)

    Article  Google Scholar 

  42. Spilka, J., Chudáček, V., Janků, P., Hruban, L., Burša, M., Huptych, M., Zach, L., Lhotská, L.: Analysis of obstetricians’ decision making on CTG recordings. J. Biomed. Inform. 51, 72–79 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DataWise Data Engineering LLC, 1938 Volberg St, Atlanta, GA, 30318, USA

    George Georgoulas

  2. Laboratory of Knowledge and Intelligent Computing, Department of Informatics and Telecommunications, University of Ioannina, 47100, Arta, Greece

    Petros Karvelis & Chrysostomos D. Stylios

  3. CIIRC, Czech Technical, University in Prague, Prague, Czech Republic

    Vaclav Chudacek, Jiri Spilka, Miroslav Burša & Lenka Lhotska

  4. Control Engineering Group, SRT Department, Luleâ University of Technology, 97187, Luleå, Sweden

    George Nikolakopoulos

Authors
  1. George Georgoulas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petros Karvelis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vaclav Chudacek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiri Spilka
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miroslav Burša
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lenka Lhotska
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chrysostomos D. Stylios
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. George Nikolakopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Petros Karvelis.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Georgoulas, G., Karvelis, P., Chudacek, V. et al. An exploratory approach to fetal heart rate–pH-based systems. SIViP 15, 43–51 (2021). https://doi.org/10.1007/s11760-020-01727-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01727-y

Keywords

Search

Navigation