Preferences help
enabled [disable] Abstract
Number of results
2017 | 132 | 3 | 451-454
Article title

Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

Title variants
Languages of publication
Cardiotocography is a monitoring technique providing important and vital information on fetal status during antepartum and intrapartum periods. The advances in modern obstetric practice allowed many robust and reliable machine learning techniques to be utilized in classifying fetal heart rate signals. The role of machine learning approaches in diagnosing diseases is becoming increasingly essential and intertwined. The main aim of the present study is to determine the most efficient machine learning technique to classify fetal heart rate signals. Therefore, the research has been focused on the widely used and practical machine learning techniques, such as artificial neural network, support vector machine, extreme learning machine, radial basis function network, and random forest. In a comparative way, fetal heart rate signals were classified as normal or hypoxic using the aforementioned machine learning techniques. The performance metrics derived from confusion matrix were used to measure classifiers' success. According to experimental results, although all machine learning techniques produced satisfactory results, artificial neural network yielded the rather well results with the sensitivity of 99.73% and specificity of 97.94%. The study results show that the artificial neural network was superior to other algorithms.
Physical description
  • Bitlis Eren University, Department of Computer Engineering, Bitlis, Turkey
  • .inönü University, Department of Computer Engineering, Malatya, Turkey
  • [1] D. Ayres-de-Campos, C.Y. Spong, E. Chandraharan, Int. J. Gynecol. Obstet. 131, 13 (2015), doi: 10.1016/j.ijgo.2015.06.020
  • [2] I. Ingemarsson, Neonatology 95, 342 (2009), doi: 10.1159/000209299
  • [3] A. Pinas, E. Chandraharan, Best Pract. Res. Clin. Obstet. Gynaecol. 30, 33 (2016), doi: 10.1016/j.bpobgyn.2015.03.022
  • [4] A.-K. Sundström, D. Rosén, K.G. Rosén, Fetal Surveillance, Neoventa Medical AB, Göteborg 2005
  • [5] M.J. Stout, A.G. Cahill, Clin. Perinatol. 38, 127 (2011), doi: 10.1016/j.clp.2010.12.002
  • [6] Z. Cömert, A.F. Kocamaz, Int. J. Comput. Appl. 156, 26 (2016), doi: 10.5120/ijca2016912417
  • [7] M.-L. Huang, Y.-Y. Hsu, J. Biomed. Sci. Eng. 5, 526 (2012), doi: 10.4236/jbise.2012.59065
  • [8] E. Yılmaz, Ç. Kılıkçıer, Comput. Math. Methods Med. 2013, 1 (2013), doi: 10.1155/2013/487179
  • [9] H. Ocak, J. Med. Syst. 37, 9913 (2013), doi: 10.1007/s10916-012-9913-4
  • [10] H. Sahin, A. Subasi, Appl. Soft Comput. 33, 231 (2015), doi: 10.1016/j.asoc.2015.04.038
  • [11] E.D. Reddy, V. Gondlekar, V. Gauns, Acta. Phys. Pol. A 130, 78 (2016), doi: 10.12693/APhysPolA.130.78
  • [12] O.F. Alcin, A. Sengur, S. Ghofrani, M.C. Ince, Measurement 55, 126 (2014), doi: 10.1016/j.measurement.2014.04.012
  • [13] D. Ayres-de-Campos, J. Bernardes, A. Garrido, J. Marques-de-Sá, L. Pereira-Leite, J. Matern. Fetal. Med. 9, 311 (2000), doi: 10.3109/14767050009053454
  • [14] Z. Cömert, A.F. Kocamaz, in: Int. Artificial Intelligence and Data Processing Symp. (IDAP), Ed: A. Karci, IDAP, Malatya (Turkey) 2016, p. 569
  • [15] J. Spilka, J. Frecon, R. Leonarduzzi, N. Pustelnik, P. Abry, M. Doret, IEEE J. Biomed. Health Inform. 21, 664 (2016), doi: 10.1109/JBHI.2016.2546312
  • [16] Z. Cömert, A.F. Kocamaz, in: 25th Signal Processing and Communications Applications Conf. (SIU), Antalya (Turkey) 2017, p. 1, doi: 10.1109/SIU.2017.7960397
  • [17] G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Neurocomputing 70, 489 (2006), doi: 10.1016/j.neucom.2005.12.126
  • [18] Z. Cömert, A.F. Kocamaz, S. Gungor, in: 24th Signal Processing and Communication Application Conf. (SIU), 2016, doi: 10.1109/siu.2016.7496034
  • [19] K. Warwick, R. Craddock, IEEE Conf. Decis. Control 1, 464 (1996), doi: 10.1109/CDC.1996.574355
  • [20] P. Tomáš, J. Krohová, P. Dohnálek, P. Gajdoš, in: 36th Int. Conf. on Telecommunications and Signal Processing (TSP), IEEE, Rome 2013, p. 620, doi: 10.1109/TSP.2013.6614010
  • [21] D. Marquardt, J. Soc. Ind. Appl. Math. 11, 431 (1963), doi: 10.1137/0111030
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.