Feature Extraction of Cardiotocography Signal

  • A. Usha SriEmail author
  • M. Malini
  • G. Chandana
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


Fetal heart activity is a vital measurement to assess the well-being of fetus throughout its intrauterine lifetime and mostly at the time of delivery. As it is a fact that the fetal heart rate interpretations are done manually, the readings are highly inaccurate and found to be subjective. Automated CTG analysis has been adopted as the most capable way to handle these problems of CTG. In this scope, CTG-OAS, an open software is used for fetal heart rate analysis. This software analyses the fetal heart rate and extracts the features of heart rate variability for further analysis. The results obtained are validated with those derived from the pH values of the cortical blood samples of delivered babies. The sympathetic and parasympathetic control on fetal heart rate and its relation with the fetal oxygenation is studied and analyzed for early detection of fetal distress.


Fetal Heart Rate (FHR) Cardiotocography CTG-OAS software CTU-UHB database 


  1. 1.
    Mendez-Bauer C, Arnt IC, Gulin L, Escarcena L, Caldeyro-Barcia R (1967) Relationship between blood pH and heart rate in the human fetus during labor. Am J Obstet Gynecol 97(4):530–545CrossRefGoogle Scholar
  2. 2.
    Khandoker AH, Karmakar C, Kimura Y, Palaniswami M (2013) Development of fetal heart rate dynamics before and after 30 and 35 weeks of gestation. Comput Cardiol 40:453–456. ISSN 2325-8861Google Scholar
  3. 3.
    Khandpur RS (2003) Hand book of biomedical engineering, 2nd edn. Tata McGraw-Hill Education, New YorkGoogle Scholar
  4. 4.
    Ayres-de-Campos D, Spong CY, Chandraharan E (2015) FIGO consensus guidelines on intrapartum fetal monitoring: cardiotocography. Int J Gynecol Obstet 131(1):13–24CrossRefGoogle Scholar
  5. 5.
    Cömert Z, Kocamaz AF (2017) A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”. In: International artificial intelligence and data processing symposium.
  6. 6.
    Cömert Z, Kocamaz AF, Gungor S (2016) Cardiotocography signals with artificial neural network and extreme learning machine. In: 24th signal processing and communication application conference (SIU).
  7. 7.
    Ayres-de-Campos D, Rei M, Nunes I, Sousa P, Bernardes J (2017) SisPorto 4.0 - computer analysis following the 2015 FIGO guidelines for intrapartum fetal monitoring. J Matern Fetal Neonatal Med 30(1):62–67. Epub 2016 Mar 29CrossRefGoogle Scholar
  8. 8.
    Cömert Z, Kocamaz AF (2016) Evaluation of fetal distress diagnosis during delivery stages based on linear and nonlinear features of fetal heart rate for neural network community. Int J Comput Appl 156(4):26–31Google Scholar
  9. 9.
    Spilka J et al (2012) Using nonlinear features for fetal heart rateclassification. Biomed Signal Process Control 7(4):350–357CrossRefGoogle Scholar
  10. 10.
    Signorini MG, Magenes G, Cerutti S, Arduini D (2003) Linear and nonlinear parameters for the analysis of fetalheart rate signal from cardiotocographic recordings. IEEE Trans Biomed Eng 50(3):365–374CrossRefGoogle Scholar
  11. 11.
    Behar J, Andreotti F, Zaunseder S, Oster J, Clifford GD (2016) A practical guide to non-invasive foetal electrocardiogram extraction and analysis. Physiol Meas 37(5):1–35CrossRefGoogle Scholar
  12. 12.
    Bernardes J, Moura C, de Sa JP, Leite LP (1991) The Porto system for automated cardiotocographic signalanalysis. J Perinat Med 19(1–2):61–65CrossRefGoogle Scholar
  13. 13.
    Gonçalves H, Rocha AP, Ayres-de-Campos D, Bernardes J (2006) Linear and nonlinear fetal heart rate analysis of normal and acidemic fetuses in the minutes preceding delivery. Med Biol Eng Comput 44(10):847CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringOsmania UniversityHyderabadIndia

Personalised recommendations