Biomaterials and Biomechanics in Bioengineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal

  • Arif, Muhammad (Department of Computer Science, College of Computer and Information systems, Umm-Alqura University, KSA)
  • Received : 2015.03.19
  • Accepted : 2015.11.06
  • Published : 2015.09.25
⟨ Previous Next ⟩

Abstract

In obstetrics, cardiotocography is a procedure to record the fetal heartbeat and the uterine contractions usually during the last trimester of pregnancy. It helps to monitor patterns associated with the fetal activity and to detect the pathologies. In this paper, random forest classifier is used to classify normal, suspicious and pathological patterns based on the features extracted from the cardiotocograms. The results showed that random forest classifier can detect these classes successfully with overall classification accuracy of 93.6%. Moreover, important features are identified to reduce the feature space. It is found that using seven important features, similar classification accuracy can be achieved by random forest classifier (93.3%).

Keywords

Acknowledgement

Supported by : Umm Al-Qura University

References

  1. Arif, M., Bilal, M., Kattan, A. and Ahamed, S.I. (2014), "Better physical activity classification using Smartphone acceleration sensor", J. Med. Syst., 38(9), 1-10. https://doi.org/10.1007/s10916-013-0001-1
  2. Ayres-de-Campos, D., Bernardes, J., Garrido, A., Marques-de-Sa, J. and Pereira-Leite, L. (2000), "SisPorto 2.0: a program for automated analysis of cardiotocograms", J. Maternal-Fetal Neonatal Med., 9(5), 311-318. https://doi.org/10.1002/1520-6661(200009/10)9:5<311::AID-MFM12>3.3.CO;2-0
  3. Ayres-de-Campos, D., Sousa, P., Costa, A. and Bernardes, J. (2008), "Omniview-$SisPorto^{(R)}$ 3.5-a central fetal monitoring station with online alerts based on computerized cardiotocogram+ ST event analysis", J. Perinatal Med., 36(3), 260-264. https://doi.org/10.1515/JPM.2008.030
  4. Breiman, L. (2001), "Random forests", Machine Learn., 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
  5. Brown, R., Wijekoon, J.H., Fernando, A., Johnstone, E.D. and Heazell, A.E. (2014), "Continuous objective recording of fetal heart rate and fetal movements could reliably identify fetal compromise, which could reduce stillbirth rates by facilitating timely management", Med. Hypotheses, 83(3), 410-417. https://doi.org/10.1016/j.mehy.2014.07.009
  6. Carbonne, B., Langer, B., Goffinet, F., Audibert, F., Tardif, D., Le Goueff, F. and French Study Group on Fetal Pulse Oximetry (1997), "Multicenter study on the clinical value of fetal pulse oximetry", Am. J. Obstetric. Gynecol., 177(3), 593-598. https://doi.org/10.1016/S0002-9378(97)70151-0
  7. Chen, C.Y., Yu, C., Chang, C.C. and Lin, C.W. (2014), Comparison of a novel computerized analysis program and visual interpretation of cardiotocography, e112296.
  8. Chen, H.Y., Chauhan, S.P., Ananth, C.V., Vintzileos, A.M. and Abuhamad, A.Z. (2011), "Electronic fetal heart rate monitoring and its relationship to neonatal and infant mortality in the United States", Am. J. Obstetric. Gynecol., 204(6), 491-e1.
  9. Chen, X., Ye, Y., Xu, X. and Huang, J.Z. (2012), "A feature group weighting method for subspace clustering of high-dimensional data", Patt0 Recognition, 45(1), 434-446. https://doi.org/10.1016/j.patcog.2011.06.004
  10. Costa, M.D., Schnettler, W.T., Amorim-Costa, C., Bernardes, J., Costa, A., Goldberger, A.L. and Ayres-de-Campos, D. (2014), "Complexity-loss in fetal heart rate dynamics during labor as a potential biomarker of acidemia", Ear. Human Develop., 90(1), 67-71. https://doi.org/10.1016/j.earlhumdev.2013.10.002
  11. Cruz, R.M., Sabourin, R., Cavalcanti, G.D. and Ren, T.I. (2015), "META-DES: A dynamic ensemble selection framework using meta-learning", Patt. Recognition, 48(5), 1925-1935. https://doi.org/10.1016/j.patcog.2014.12.003
  12. Czabanski, R., Jezewski, J., Matonia, A. and Jezewski, M. (2012), "Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia", Expert Syst. Appl., 39(15), 11846-11860. https://doi.org/10.1016/j.eswa.2012.01.196
  13. Czabanski, R., Jezewski, M., Wrobel, J., Horoba, K. and Jezewski, J. (2008, January), "A neuro-fuzzy approach to the classification of fetal cardiotocograms", In 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, Springer Berlin Heidelberg.
  14. Dietterich, T.G. (2000), "Ensemble methods in machine learning", In Multiple classifier systems, Springer Berlin Heidelberg.
  15. Dietterich, T.G. (2000), "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization", Machine Learn.., 40(2), 139-157. https://doi.org/10.1023/A:1007607513941
  16. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. and Herrera, F. (2012), "A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. Systems, Man, and Cybernetics, Part C: Applications and Reviews", IEEE Trans., 42(4), 463-484.
  17. Georgieva, A., Payne, S.J., Moulden, M. and Redman, C.W. (2013), "Artificial neural networks applied to fetal monitoring in labour", Neural Comput. Appl., 22(1), 85-93. https://doi.org/10.1007/s00521-011-0743-y
  18. Goncalves, H., Rocha, A.P., Ayres-de-Campos, D. and 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), 847-855. https://doi.org/10.1007/s11517-006-0105-6
  19. Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D. and Alzheimer's Disease Neuroimaging Initiative (2013), "Random forest-based similarity measures for multi-modal classification of Alzheimer's disease", Neuroimage, 65, 167-175. https://doi.org/10.1016/j.neuroimage.2012.09.065
  20. Grivell, R.M., Alfirevic, Z., Gyte, G.M. and Devane, D. (2010), "Antenatal cardiotocography for fetal assessment", Cochrane Database Syst. Rev., 1.
  21. Jezewski, M., Wrobel, J., Labaj, P., Leski, J., Henzel, N., Horoba, K. and Jezewski, J. (2007), "Some practical remarks on neural networks approach to fetal cardiotocograms classification", In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, IEEE.
  22. Jezewski, M. and Eski, J.L. (2014), "The influence of cardiotocogram signal feature selection method on fetal state assessment efficacy".
  23. Kandaswamy, K.K., Chou, K.C., Martinetz, T., Moller, S., Suganthan, P.N., Sridharan, S. and Pugalenthi, G. (2011), "AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties", J. Theo. Biol., 270(1), 56-62. https://doi.org/10.1016/j.jtbi.2010.10.037
  24. Karabulut, E.M. and Ibrikci, T. (2014), "Analysis of cardiotocogram data for fetal distress determination by decision tree based adaptive boosting approach", J. Comput. Commun., 2(09): 32.
  25. Kwon, J.Y., Park, I.Y., Shin, J.C., Song, J., Tafreshi, R. and Lim, J. (2012), "Specific change in spectral power of fetal heart rate variability related to fetal acidemia during labor: comparison between preterm and term fetuses", Ear. Human Develop., 88(4), 203-207. https://doi.org/10.1016/j.earlhumdev.2011.08.007
  26. Lees, C., Marlow, N., Arabin, B., Bilardo, C.M., Brezinka, C., Derks, J.B. and Wolf, H. (2013), "Perinatal morbidity and mortality in early-onset fetal growth restriction: cohort outcomes of the trial of randomized umbilical and fetal flow in Europe (TRUFFLE)", Ultrasound Obstetric. Gynecol., 42(4), 400-408. https://doi.org/10.1002/uog.13190
  27. Logier, R., Jonckheere, J.D., Jeanne, M. and Matis, R. (2008), "Fetal distress diagnosis using heart rate variability analysis: design of a high frequency variability index", In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, IEEE.
  28. Macones, G.A., Hankins, G.D., Spong, C.Y., Hauth, J. and Moore, T. (2008), "The 2008 National Institute of Child Health and Human Development workshop report on electronic fetal monitoring: update on definitions, interpretation, and research guidelines", J. Obstetric. Gynecol. Neonatal Nurs., 37(5), 510-515. https://doi.org/10.1111/j.1552-6909.2008.00284.x
  29. Menai, M.E.B., Mohder, F.J. and Al-mutairi, F. (2013), "Influence of feature selection on naive Bayes classifier for recognizing patterns in cardiotocograms", J. Med. Bioeng., 2(1).
  30. Ocak, H. (2013), "A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being", J. Med. Syst., 37(2), 1-9. https://doi.org/10.1007/s10916-013-9926-7
  31. Ozcift, A. (2012), "Enhanced cancer recognition system based on random forests feature elimination algorithm", J. Med. Syst., 36(4), 2577-2585. https://doi.org/10.1007/s10916-011-9730-1
  32. Salamalekis, E., Hintipas, E., Salloum, I., Vasios, G., Loghis, C., Vitoratos, N. and Creatsas, G. (2006), "Computerized analysis of fetal heart rate variability using the matching pursuit technique as an indicator of fetal hypoxia during labor", J. Maternal-Fetal Neonatal Med., 19(3), 165-169. https://doi.org/10.1080/14767050500233290
  33. Spencer, J.A. (1993), "Clinical overview of cardiotocography", BJOG: Int. J. Obstetrics Gynaecol., 100(s9), 4-7. https://doi.org/10.1111/j.1471-0528.1993.tb10626.x
  34. Sundar, C., Chitradevi, M. and Geetharamani, G. (2013), "An overview of research challenges for classification of cardiotocogram data", J. Comput. Sci., 9(2), 198. https://doi.org/10.3844/jcssp.2013.198.206
  35. Svetnik, V., Wang, T., Tong, C., Liaw, A., Sheridan, R.P. and Song, Q. (2005), "Boosting: An ensemble learning tool for compound classification and QSAR modeling", J. Chem. Inform. Model., 45(3), 786-799. https://doi.org/10.1021/ci0500379
  36. Tibshirani, R. (1996), Bias, variance and prediction error for classification rules, University of Toronto, Department of Statistics.
  37. Ugwumadu, A. (2013), "Understanding cardiotocographic patterns associated with intrapartum fetal hypoxia and neurologic injury", Best Practice Res. Clin. Obstetric. Gynaecol., 27(4), 509-536. https://doi.org/10.1016/j.bpobgyn.2013.04.002
  38. van der Hout-van, M.B., Oei, S.G. and Bovendeerd, P.H. (2012), "A mathematical model for simulation of early decelerations in the cardiotocogram during labor", Med. Eng. Phys., 34(5), 579-589. https://doi.org/10.1016/j.medengphy.2011.09.004
  39. Wolpert, D.H. and Macready, W.G. (1999), "An efficient method to estimate bagging's generalization error", Machine Learn., 35(1), 41-55. https://doi.org/10.1023/A:1007519102914
  40. Zhou, J. and Sun, S. (2014), "Active learning of Gaussian processes with manifold-preserving graph reduction", Neural Comput. Appl., 25(7-8), 1615-1625. https://doi.org/10.1007/s00521-014-1643-8

Cited by

  1. A systematic review of automated pre-processing, feature extraction and classification of cardiotocography vol.7, pp.None, 2015, https://doi.org/10.7717/peerj-cs.452