Skip to main content
SpringerLink
Log in

Cardiotocograph-based labor stage classification from uterine contraction pressure during ante-partum and intra-partum period: a fuzzy theoretic approach

  • Research
  • Published:
Health Information Science and Systems Aims and scope Submit manuscript

Abstract

Computerized techniques for Cardiotocograph (CTG) based labor stage classification would support obstetrician for advance CTG analysis and would improve their predictive power for fetal heart rate (FHR) monitoring. Intrapartum fetal monitoring is necessary as it can detect the event, which ultimately leads to hypoxic ischemic encephalopathy, cerebral palsy or even fetal demise. To bridge this gap, in this paper, we propose an automated decision support system that will help the obstetrician identify the status of the fetus during ante-partum and intra-partum period. The proposed algorithm takes 30 min of 275 Cardiotocograph data and applies a fuzzy-rule based approach for identification and classification of labor from ‘toco’ signal. Since there is no gold standard to validate the outcome of the proposed algorithm, the authors used various statistical means to establish the cogency of the proposed algorithm and the degree of agreement with visual estimation were using Bland–Altman plot, Fleiss kappa (0.918 ± 0.0164 at 95% CI) and Kendall’s coefficient of concordance (W = 0.845). Proposed method was also compared against some standard machine learning classifiers like SVM, Random Forest and Naïve Bayes using weighted kappa (0.909), Bland–Altman plot (Limits of Agreement 0.094 to 0.0155 at 95% CI) and AUC-ROC (0.938). The proposed algorithm was found to be as efficient as visual estimation compared to the standard machine learning algorithms and thus can be incorporated into the automated decision support system.

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

References

  1. Teegala B, Kalyansundar A, Ramesh B. Uterine contraction measurement device and a fetal monitoring system. 2018. WO 2011/023521 Al.

  2. Ricci SS, Kyle T. Maternity and pediatric nursing. New York: Lippincott William and Wilkins; 2009.

    Google Scholar 

  3. Das S, Roy K, Saha CK. A novel step towards machine diagnosis of fetal status in utero: Calculation of baseline variability. In Proceedings of 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). Kolkata: IEEE Press; 2015, p. 230–234. https://doi.org/10.1109/icrcicn.2015.743424.

  4. Dawes GS, Redman CWG. Numerical analysis of the human fetal heart rate: the quality of ultrasound records. Am J Obstet Gynecol. 1981;141(1):43–52.

    Article  Google Scholar 

  5. Alonso Betanzos A, Moret Bonillo V, Devoe LD, Searle JR, Boveda Alvarez C. NST EXPERT: an Intelligent Program For NST Interpretation. Artif Intell Med. 1995;7(4):297–313.

    Article  Google Scholar 

  6. Das S, Roy K, Saha CK. A linear time series analysis of fetal heart rate to detect the variability: measures using cardiotocography. In Bhattacharyya S, Das N, Bhattacharjee D, Mukherjee A, editors. Handbook of research on recent developments in intelligent communication application (pp. 471-495). Hershey, PA: IGI Global. 2017. https://doi.org/10.4018/978-1-5225-1785-6.ch018.

  7. Guijarro-Berdiñas B, Alonso-Betanzos A, Prados-Méndez S, Fernández-Chaves O, Alvarez-Seoane M, Ucieda-Pardinas F. A hybrid intelligent system for the pre-processing of Fetal Heart rate signals in antenatal testing. In: Mira J, Moreno-Díaz R, Cabestany J, editors. Biological and artificial computation: from neuroscience to technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Berlin, Heidelberg: Springer; 1997.

  8. Magenes G, Signorini M, Ferrario M, Lunghi F. 2CTG2: A new system for the antepartum analysis of fetal heart rate. In: Jarm T, Kramar P, Zupanic A, editors. IFMBE Proceedings of 11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007, vol 16. Berlin, Heidelberg: Springer; 2007.

  9. Cömert Z, Şengür A, Budak Ü, Kocamaz AF. Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models. Health Inf Sci Syst. 2019;7(1):17.

    Article  Google Scholar 

  10. Winn HC, Hobbins JC. Clinical maternal-fetal medicine. New York: The Parthenon Publishing Group; 2000.

    Google Scholar 

  11. Cunnigham FG, Leveno KJ, Bloom SL, Spong CY, Dashe JS, Hoffman BL, Casey BM. Williams obstetrics. New York: McGraw Hill; 2001.

    Google Scholar 

  12. Macones GA, Hankins GD, Spong CY, Hauth J, Moore T. The 2008 National Institute of Child Health and Human Development workshop report on electronic fetal monitoring: update on definitions, interpretation, and research guidelines. J Obstet Gynecol Neonatal Nurs. 2008;37(5):510–5. https://doi.org/10.1111/j.1552-6909.2008.00284.xPMID:18761565.

    Article  Google Scholar 

  13. ACOG Practice Bulletin No. 106: Intrapartum fetal heart rate monitoring: nomenclature, interpretation, and general management principles (2009), Obstet. Gynecol. pp. 192–202.

  14. Bakkar PC, Kurver PH, Kuik DJ, Van Geijn HP. Elevated uterine activity increases the risk of fetal acidosis at birth. Am J Obstet Gynecol. 2007;196(4):331.

    Google Scholar 

  15. Maojo V, Sannanders J, Billhartdt H, Crespo J. Computational intelligence techniques in medical decision making: the data mining perspective. In: Schmitt M, Teodorescu HN, Jain A, Jain A, Jain S, editors. Studies in fuzziness and soft computing, vol. 96. Heidelberg: Physica; 2002. p. 13–44.

    Google Scholar 

  16. Konoenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. 2001;23(1):89–109.

    Article  Google Scholar 

  17. Das S, Roy K, Saha CK. Fuzzy membership estimation using ANN: a case study in CTG analysis. In: Satapathy S, Biswal B, Udgata S, Mandal J, editors. Advances in intelligent systems and computing, vol. 327. Cham: Springer; 2015. p. 221–8.

    Google Scholar 

  18. Czech Technical University (CTU) in Prague and University Hospital in Brno (UHB) database. 2010. http://physionet.nlm.nih.gov/pn3/ctu-uhb-ctgdb/. Accessed 8 Aug 2018

  19. Sakpal PV. Sample size estimation in clinical trial. Perspect Clin Res. 2010;1(2):67–9.

    Google Scholar 

  20. Lawson AE, Daniel ES. Inferences of clinical diagnostic reasoning and diagnostic error. J Biomed Inform. 2011;44(3):402–12.

    Article  Google Scholar 

  21. Das S, Guha D, Dutta B. Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic. Appl Intell. 2016;44(3):850–7.

    Article  Google Scholar 

  22. Jang JSR, Sun CT, Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Upper Saddle River: Prentice Hall; 1997.

    Google Scholar 

  23. Kwiecien R, Kopp-Schneider A, Blettner M. Concordance analysis: Part 16 of a series on evaluation of scientific publications. Dtsch Arztebl Int. 2011;108(30):515–21.

    Google Scholar 

  24. Altman DG, Bland JM. Design, analysis, and interpretation of method comparison-studies. AACN Adv Crit Care. 2008;19(2):223–34 PMID:18560291.

    Google Scholar 

  25. Zainotz C. Fleiss’ Kappa. 2014. http://www.real-statistics.com/reliability/fleiss-kappa/ Accessed 8 Aug 2018.

  26. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med. 2012;22(3):276–82.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, West Bengal State University, Kolkata, 700124, West Bengal, India

    Sahana Das & Kaushik Roy

  2. Department of Computer Science & Engineering, Aliah University, Kolkata, 700156, India

    Sk Md Obaidullah

  3. Department of Computer Science, The University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  4. Department of Obstetrics & Gynecology, Biraj Mohini Matri-Sadan & Hospital, Kolkata, West Bengal, 700122, India

    Chanchal Kumar Saha

Authors
  1. Sahana Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sk Md Obaidullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. C. Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaushik Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chanchal Kumar Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaushik Roy.

Ethics declarations

Conflict of interest

Authors declare that they have no conflicts of interest.

Research involving human participants or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

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

Das, S., Obaidullah, S.M., Santosh, K.C. et al. Cardiotocograph-based labor stage classification from uterine contraction pressure during ante-partum and intra-partum period: a fuzzy theoretic approach. Health Inf Sci Syst 8, 16 (2020). https://doi.org/10.1007/s13755-020-00107-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13755-020-00107-7

Keywords

Search

Navigation