International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN. 

Risk Prediction of Fetal Heart Rate from Cardiotocography Using Artificial Intelligence and Machine Learning

    ,

Author Affiliations

  • 1Department of Computer Science, CAET, Etawah, UP, India
  • 2Department of Electronics and Communication Engineering, CAET, Etawah, UP, India

Res. J. Computer & IT Sci., Volume 14, Issue (1), Pages 23-25, June,20 (2026)

Abstract

Cardiotocography (CTG), which records the fetal heart rate (FHR) and uterine contractions (UC), is the cornerstone of fetal monitoring during pregnancy and labor. However, its manual interpretation is highly subjective, leading to significant inter and intra-observer variability and high false-positive rates for fetal distress. This review paper synthesizes recent advancements in applying Artificial Intelligence (AI) and Machine Learning (ML) techniques to CTG data to develop objective, accurate, and automated systems for predicting fetal risk. We explore the spectrum of ML algorithms employed, features extracted from CTG signals, performance metrics, and persistent challenges including data scarcity and class imbalance. AI/ML integration holds immense promise for improving clinical decision-making and neonatal outcomes.

References

  1. Arnold, K.C., Flint, C.J. (2017)., Intrapartum Fetal Heart Rate Monitoring: Nomenclature, Interpretation, and General Management Principles., In: Obstetrics Essentials. Springer, Cham.
  2. Parer, J. T., & King, T. (2000)., Fetal heart rate monitoring: Is it salvageable?., American Journal of Obstetrics and Gynecology, 182(4), 982–987.
  3. Ayres-de-Campos, D., Spong, C.Y., Chandraharan, E. and FIGO Intrapartum Fetal Monitoring Expert Consensus Panel (2015)., FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography., International Journal of Gynecology & Obstetrics, 131, 13-24.
  4. Hruban, L., Spilka, J., Chudáček, V., Janků, P., Huptych, M., Burša, M., Styblík, J., & Lhotská, L. (2015)., Agreement on intrapartum cardiotocogram recordings between expert obstetricians., Journal of Evaluation in Clinical Practice, 21(4), 694–702.
  5. Clark, S. L., Hamilton, E. F., Garite, T. J., Timmins, A., Warrick, P. A., & Smith, S. (2017)., The limits of electronic fetal heart rate monitoring in the prevention of neonatal metabolic acidemia., American Journal of Obstetrics and Gynecology, 216(2), 163.e1–163.e6.
  6. Georgieva, A., Abry, P., Chudáček, V., Djurić, P. M., Frasch, M. G., Kok, R., ... & Vullings, R. (2019)., Computer‐based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor., Acta obstetricia et gynecologica Scandinavica, 98(9), 1207-1217.
  7. Aeberhard, J. L., Radan, A. P., Delgado-Gonzalo, R., Strahm, K. M., Sigurthorsdottir, H. B., Schneider, S., & Surbek, D. (2023)., Artificial intelligence and machine learning in cardiotocography: A scoping review., European Journal of Obstetrics & Gynecology and Reproductive Biology, 281, 54–62.
  8. Francis, F., Luz, S., Wu, H., Stock, S. J., & Townsend, R. (2024)., Machine learning on cardiotocography data to classify fetal outcomes: A scoping review., Computers in Biology and Medicine, 172, Article 108220.
  9. Mendis, L., Palaniswami, M., Brownfoot, F., & Keenan, E. (2023)., Computerised cardiotocography analysis for the automated detection of fetal compromise during labour: a review., Bioengineering, 10(9), 1007.
  10. Ben M, Computerized cardiotocography analysis during labor–A state‐of‐the‐art review., Acta Obstetricia et Gynecologica Scandinavica, 102(2), 130-137.
  11. Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., & Lhotská, L. (2014)., Open access intrapartum CTG database., BMC pregnancy and childbirth, 14(1), 16.
  12. Spilka, J., Chudáček, V., Janků, P., Hruban, L., Burša, M., Huptych, M., Zach, L., & Lhotská, L. (2014)., Analysis of obstetricians’ decision making on CTG recordings., Journal of Biomedical Informatics, 51, 72–79.
  13. Xu, L., Redman, C. W. G., Payne, S. J., & Georgieva, A. (2014)., Feature selection using genetic algorithms for fetal heart rate analysis., Physiological Measurement, 35(7), 1357–1371.
  14. Yılmaz, E. (2016)., Fetal state assessment from cardiotocogram data using artificial neural networks., Journal of Medical and Biological Engineering, 36(6), 820-832.
  15. Arif, M. (2015)., Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal., Biomaterials and Biomechanics in Bioengineering, 2(3), 173–183.
  16. Holzinger, A. (2021)., The Next Frontier: AI We Can Really Trust., In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524. Springer.
  17. Garabedian, C., Butruille, L., Drumez, E., Servan Schreiber, E., Bartolo, S., Bleu, G., Mesdag, V., Deruelle, P., De Jonckheere, J., & Houfflin-Debarge, V. (2017)., Inter-observer reliability of 4 fetal heart rate classifications., Journal of Gynecology Obstetrics and Human Reproduction, 46(2), 131–135.
  18. Bhatia, M., Mahtani, K.R., Nunan, D. and Reddy, A. (2017)., A cross-sectional comparison of three guidelines for intrapartum cardiotocography., Int J Gynecol Obstet, 138, 89-93.