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Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach

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Book cover Software Engineering and Algorithms in Intelligent Systems (CSOC2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 763))

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Abstract

Electronic fetal monitoring (EFM) device which is used to record Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals simultaneously is one of the significant tools in terms of the present obstetric clinical applications. In clinical practice, EFM traces are routinely evaluated with visual inspection by observers. For this reason, such a subjective interpretation has been caused various conflicts among observers to arise. Although the existing of international guidelines for ensuring more consistent assessment, the automated FHR analysis has been adopted as the most promising solution. In this study, an innovative approach based on deep convolutional neural network (DCNN) is proposed to classify FHR signals as normal and abnormal. The proposed method composes of three stages. FHR signals are passed through a set of preprocessing procedures in order to ensure more meaningful input images, firstly. Then, a visual representation of time-frequency information, spectrograms are obtained with the help of the Short Time Fourier Transform (STFT). Finally, DCNN method is utilized to classify FHR signals. To this end, the colored spectrograms images are used to train the network. In order to evaluate the proposed model, we conducted extensive experiments on the open CTU-UHB database considering the area under the receiver operating characteristic curve and other several performance metrics derived from the confusion matrix. Consequently, we achieved encouraging results.

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References

  1. Murray, H.: Antenatal foetal heart monitoring. Best Pract. Res. Clin. Obstet. Gynaecol. 38, 2–11 (2017)

    Article  Google Scholar 

  2. Brown, R., Wijekoon, J.H.B., Fernando, A., Johnstone, E.D., Heazell, A.E.P.: 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, 410–417 (2014)

    Article  Google Scholar 

  3. van Geijn, H.P.: 2 Developments in CTG analysis. Baillieres Clin. Obstet. Gynaecol. 10, 185–209 (1996)

    Article  Google Scholar 

  4. Ayres-de-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. Int. J. Gynecol. Obstet. 131, 13–24 (2015)

    Article  Google Scholar 

  5. Tongsong, T., Iamthongin, A., Wanapirak, C., Piyamongkol, W., Sirichotiyakul, S., Boonyanurak, P., Tatiyapornkul, T., Neelasri, C.: Accuracy of fetal heart-rate variability interpretation by obstetricians using the criteria of the National Institute of Child Health and Human Development compared with computer-aided interpretation. J. Obstet. Gynaecol. Res. 31, 68–71 (2005)

    Article  Google Scholar 

  6. Czabanski, R., Jezewski, J., Matonia, A., Jezewski, M.: Computerized analysis of fetal heart rate signals as the predictor of neonatal acidemia. Expert Syst. Appl. 39, 11846–11860 (2012)

    Article  Google Scholar 

  7. Garabedian, C., Butruille, L., Drumez, E., Schreiber, E.S., Bartolo, S., Bleu, G., Mesdag, V., Deruelle, P., De Jonckheere, J., Houfflin-Debarge, V.: Inter-observer reliability of 4 fetal heart rate classifications. J. Gynecol. Obstet. Hum. Reprod. 46, 131–135 (2017)

    Article  Google Scholar 

  8. Palomäki, O., Luukkaala, T., Luoto, R., Tuimala, R.: Intrapartum cardiotocography: the dilemma of interpretational variation. J. Perinat. Med. 34, 298–302 (2006)

    Article  Google Scholar 

  9. Cömert, Z., Kocamaz, A.F.: Novel software for comprehensive analysis of cardiotocography signals CTG-OAS. In: KARCI, A. (ed.) International Conference on Artificial Intelligence and Data Processing (IDAP17), pp. 1–6. IEEE, Malatya (2017)

    Google Scholar 

  10. Bernardes, J., Ayres-de-Campos, D., Costa-Pereira, A., Pereira-Leite, L., Garrido, A.: Objective computerized fetal heart rate analysis. Int. J. Gynecol. Obstet. 62, 141–147 (1998)

    Article  Google Scholar 

  11. Warrick, P., Hamilton, E., Macieszczak, M.: Neural network based detection of fetal heart rate patterns. In: IEEE International Joint Conference on Neural Networks, pp. 2400–2405 (2005)

    Google Scholar 

  12. Cömert, Z., Kocamaz, A.F.: 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, 26–31 (2016)

    Google Scholar 

  13. Magenes, G., Pedrinazzi, L., Signorini, M.G.: Identification of fetal sufferance antepartum through a multiparametric analysis and a support vector machine. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 462–465 (2004)

    Google Scholar 

  14. Monteiro-Santos, J., Gonçalves, H., Bernardes, J., Antunes, L., Nozari, M., Costa-Santos, C.: Entropy and compression capture different complexity features: the case of fetal heart rate. Entropy 19, 688 (2017)

    Article  Google Scholar 

  15. Cömert, Z., Kocamaz, A.F.: Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine. In: 25th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2017)

    Google Scholar 

  16. Cömert, Z., Kocamaz, A.F.: A study based on gray level co-occurrence matrix and neural network community for determination of hypoxic fetuses. In: International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 569–573. TR (2016)

    Google Scholar 

  17. Cömert, Z., Kocamaz, A.F.: A study of artificial neural network training algorithms for classification of cardiotocography signals. Bitlis Eren Univ. J. Sci. Technol. 7, 93–103 (2017)

    Article  Google Scholar 

  18. Spilka, J., Frecon, J., Leonarduzzi, R., Pustelnik, N., Abry, P., Doret, M.: Sparse support vector machine for intrapartum fetal heart rate classification. IEEE J. Biomed. Health Inform. 21(3), 664–671 (2016)

    Article  Google Scholar 

  19. Cömert, Z., Kocamaz, A.F., Gungor, S.: Cardiotocography signals with artificial neural network and extreme learning machine. In: 24th Signal Processing and Communication Application Conference (SIU) (2016)

    Google Scholar 

  20. Sahin, H., Subasi, A.: Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Appl. Soft Comput. 33, 231–238 (2015)

    Article  Google Scholar 

  21. Cömert, Z., Kocamaz, A.F.: Comparison of machine learning techniques for fetal heart rate classification. Acta Phys. Pol. A 132, 451–454 (2017)

    Article  Google Scholar 

  22. Bursa, M., Lhotska, L.: The use of convolutional neural networks in biomedical data processing. In: Bursa, M., Holzinger, A., Renda, M.E., Khuri, S. (eds.) Proceedings of Information Technology in Bio- and Medical Informatics: 8th International Conference, ITBAM 2017, Lyon, France, 28–31 August 2017, pp. 100–119. Springer, Cham (2017)

    Chapter  Google Scholar 

  23. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105. Curran Associates, Inc., USA (2012)

    Google Scholar 

  24. Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy Childbirth 14, 16 (2014)

    Article  Google Scholar 

  25. Cesarelli, M., Romano, M., Bifulco, P., Fedele, F., Bracale, M.: An algorithm for the recovery of fetal heart rate series from CTG data. Comput. Biol. Med. 37, 663–669 (2007)

    Article  Google Scholar 

  26. Spilka, J., Georgoulas, G., Karvelis, P., Oikonomou, V.P., Chudáček, V., Stylios, C., Lhotská, L., Jankru, P.: Automatic evaluation of FHR recordings from CTU-UHB CTG database. In: Bursa, M., Khuri, S., Renda, M.E. (eds.) Proceedings of Information Technology in Bio- and Medical Informatics: 4th International Conference, ITBAM 2013, Prague, Czech Republic, 28 August 2013, pp. 47–61. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  27. Kahaner, D., Moler, C., Nash, S.: Numerical Methods and Software. Prentice-Hall Inc., Upper Saddle River (1989)

    MATH  Google Scholar 

  28. Romano, M., Faiella, G., Bifulco, P., D’Addio, G., Clemente, F., Cesarelli, M.: Outliers detection and processing in CTG monitoring. In: Roa Romero, L.M. (ed.) XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, MEDICON 2013, Seville, Spain, 25–28 September 2013, pp. 651–654. Springer, Cham (2014)

    Google Scholar 

  29. Nawab, S., Quatieri, T., Lim, J.: Signal reconstruction from short-time Fourier transform magnitude. IEEE Trans. Acoust. 31, 986–998 (1983)

    Article  Google Scholar 

  30. Romano, M., Iuppariello, L., Ponsiglione, A.M., Improta, G., Bifulco, P., Cesarelli, M.: Frequency and time domain analysis of foetal heart rate variability with traditional indexes: a critical survey. Comput. Math Methods Med. 2016, 1–12 (2016)

    Article  Google Scholar 

  31. Groome, L.J., Mooney, D.M., Bentz, L.S., Singh, K.P.: Spectral analysis of heart rate variability during quiet sleep in normal human fetuses between 36 and 40 weeks of gestation. Early Hum. Dev. 38, 1–9 (1994)

    Article  Google Scholar 

  32. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Google Scholar 

  33. Yu, Y., Lin, H., Meng, J., Wei, X., Guo, H., Zhao, Z.: Deep transfer learning for modality classification of medical images. Information 8(3), 91 (2017)

    Article  Google Scholar 

  34. Subha, V., Murugan, D.: Genetic Algorithm based feature subset selection for fetal state classification. J. Commun. Technol. Electron. Comput. Sci. 2, 13–17 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Bitlis Eren University, Bitlis, Turkey

    Zafer Cömert

  2. İnönü University, Malatya, Turkey

    Adnan Fatih Kocamaz

Authors
  1. Zafer Cömert
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  2. Adnan Fatih Kocamaz
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Correspondence to Zafer Cömert .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

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Cömert, Z., Kocamaz, A.F. (2019). Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach. In: Silhavy, R. (eds) Software Engineering and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 763. Springer, Cham. https://doi.org/10.1007/978-3-319-91186-1_25

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