Skip to main content
SpringerLink
Log in

A Shallow 1-D Convolution Neural Network for Fetal State Assessment Based on Cardiotocogram

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Cardiotocography consists of fetal heart rate and uterine contraction signals that have been utilized for fetal well-being assessment. Researchers have applied several machine learning methods to improve the classification accuracy of the fetal state assessment. However, the proposed methods do not fulfill the required accuracy, as they have to address signal challenges such as missing value and external noise. Recently, convolutional neural networks have been brought to researchers' attention to cope with the challenges above in other machine learning applications. In this article, a new shallow architecture of 1-D convolution neural network is proposed to enhance fetal state assessment accuracy. This architecture has performed based on one convolution layer, resulting in computational complexity reduction. Besides, pooling operation that is a standard part of traditional CNN is not applied in this architecture to have more features in the classification phase. The performance of the proposed architecture is evaluated using five different clinical data sets. The results show that the proposed architecture is more efficient than traditional 1-D CNN and five implemented classifiers. The proposed architecture also achieves very competitive accuracy in the fetal state assessment compared to previous researches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Yilmaz E, Kilikçier Ç. Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree. Comput Math Methods Med. 2013. https://doi.org/10.1155/2013/487179.

    Article  MATH  Google Scholar 

  2. Ocak H. A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being. J Med Syst. 2013. https://doi.org/10.1007/s10916-012-9913-4.

    Article  Google Scholar 

  3. Yılmaz E. Fetal State Assessment from cardiotocogram data using artificial neural networks. J Med Biol Eng. 2016;36:820–32. https://doi.org/10.1007/s40846-016-0191-3.

    Article  Google Scholar 

  4. Tsipouras MG, Tsouros DC, Smyrlis PN, Giannakeas N, Tzallas AT. Random forests with stochastic induction of decision trees. In: Proceedings of the international conference tools with artificial intelligence. ICTAI. 2018-Nov, pp. 527–531. 2018. https://doi.org/10.1109/ICTAI.2018.00087.

  5. Ravindran S, Jambek AB, Muthusamy H, Neoh S-C. A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being. Comput. Math. Methods Med. 2015, (2015).

  6. Comert Z, Kocamaz AF, Gungor S. Classification and comparison of cardiotocography signals with artificial neural network and extreme learning machine. In: 2016 24th signal processing and communication applications conference on SIU 2016—proceedings, pp. 1493–1496. 2016. https://doi.org/10.1109/SIU.2016.7496034.

  7. Dehkordi MR, Seifzadeh H, Beydoun G, Nadimi-Shahraki MH. Success prediction of android applications in a novel repository using neural networks. Complex Intell Syst. 2020. https://doi.org/10.1007/s40747-020-00154-3.

    Article  Google Scholar 

  8. Zhao B, Lu H, Chen S, Liu J, Wu D. Convolutional neural networks for time series classification. J Syst Eng Electron. 2017;28:162–9. https://doi.org/10.21629/JSEE.2017.01.18.

    Article  Google Scholar 

  9. Fasihi M, Nadimi-Shahraki MH, Jannesari A. Multi-class cardiovascular diseases diagnosis from electrocardiogram signals using 1-D convolution neural network. In: Proceedings on 2020 IEEE 21st international conference on information reuse and integration for data sciences, IRI 2020. 372–378. 2020. https://doi.org/10.1109/IRI49571.2020.00060.

  10. Dua D, Graff C. UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 1 Nov 2019

  11. Sajda P. Machine learning for detection and diagnosis of disease. Annu Rev Biomed Eng. 2006;8:537–65. https://doi.org/10.1146/annurev.bioeng.8.061505.095802.

    Article  Google Scholar 

  12. Zamani H, Nadimi-Shahraki M-H. Swarm intelligence approach for breast cancer diagnosis. Int J Comput Appl. 2016;151:40–4. https://doi.org/10.5120/ijca2016911667.

    Article  Google Scholar 

  13. Sharma M. Cervical cancer prognosis using genetic algorithm and adaptive boosting approach. Health Technol (Berl). 2019. https://doi.org/10.1007/s12553-019-00375-8.

    Article  Google Scholar 

  14. Koloseni D, Lampinen J, Luukka P. Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data sets. Expert Syst Appl. 2013;40:4075–82.

    Article  Google Scholar 

  15. Arjenaki HG, Nadimi-Shahraki MH, Nourafza N. A low cost model for diagnosing coronary artery disease based on effective features. Int J Electron Commun Comput Eng. 2015;6:93–7.

    Google Scholar 

  16. Khademi M, Nedialkov NS. Probabilistic graphical models and deep belief networks for prognosis of breast cancer. In: Proceedings—2015 IEEE 14th international conference on machine learning and applications, ICMLA 2015, Institute of Electrical and Electronics Engineers Inc., pp 727–732. 2016. https://doi.org/10.1109/ICMLA.2015.196.

  17. Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V. Massively multitask networks for drug discovery. arXiv Prepr. arXiv:1502.02072. (2015).

  18. Lipton ZC, Kale DC, Elkan C, Wetzell R. Learning to diagnose with LSTM recurrent neural networks. arXiv Prepr. arXiv:1511.03677. (2015).

  19. Gupta P, Malhi AK. Using deep learning to enhance head and neck cancer diagnosis and classification. In: 2018 IEEE International conference on system, computation, automation and networking, ICSCA 2018. 2018. https://doi.org/10.1109/ICSCAN.2018.8541142.

  20. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–8. https://doi.org/10.1038/nature21056.

    Article  Google Scholar 

  21. Payan A, Montana G. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks, pp. 1–9. 2015. https://doi.org/10.1613/jair.301.

  22. Subbulakshmi CV, Deepa SN. Medical dataset classification: a machine learning paradigm integrating particle swarm optimization with extreme learning machine classifier. Sci World J. 2015;2015

    Article  Google Scholar 

  23. Toprak A. Extreme learning machine (ELM)-based classification of benign and malignant cells in breast cancer. Med Sci Monit. 2018;24:6537–43. https://doi.org/10.12659/MSM.910520.

    Article  Google Scholar 

  24. Giri EP, Fanany MI, Arymurthy AM, Wijaya SK. Ischemic stroke identification based on EEG and EOG using ID convolutional neural network and batch normalization. In: 2016 International conference on advanced computer science and information systems ICACSIS 2016. pp. 484–491. 2017. https://doi.org/10.1109/ICACSIS.2016.7872780.

  25. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, Tan RS. A deep convolutional neural network model to classify heartbeats. Comput Biol Med. 2017;89:389–96. https://doi.org/10.1016/j.compbiomed.2017.08.022.

    Article  Google Scholar 

  26. Kiranyaz S, Ince T, Gabbouj M. Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng. 2016;63:664–75. https://doi.org/10.1109/TBME.2015.2468589.

    Article  Google Scholar 

  27. Cömert Z, Kocamaz A. A study of artificial neural network training algorithms for classification of cardiotocography signals. Bitlis Eren Univ J Sci Technol. 2017;7:93–103. https://doi.org/10.1768/beuscitech.338085.

    Article  Google Scholar 

  28. Piri J, Mohapatra P, Dey R. Fetal health status classification using MOGA—CD based feature selection approach. In: Proceedings on CONECCT 2020—6th IEEE international conference on electronics, computing and communication technologies. 2020. https://doi.org/10.1109/CONECCT50063.2020.9198377.

  29. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu Perez J, Lo B, Yang G-Z. Deep learning for health informatics. IEEE J Biomed Health Inform. 2016;21:1–1. https://doi.org/10.1109/JBHI.2016.2636665.

    Article  Google Scholar 

  30. He K, Sun J. Convolutional neural networks at constrained time cost. In: Proceedings on IEEE comput. Soc. Conference on computer vision and pattern recognition, 07–12-June, pp. 5353–5360. 2015. https://doi.org/10.1109/CVPR.2015.7299173.

  31. Jin L, Dong J. Ensemble deep learning for biomedical time series classification. Comput. Intell. Neurosci. 2016, (2016).

  32. Zheng Y, Liu Q, Chen E, Ge Y, Zhao JL. Time series classification using multi-channels deep convolutional neural networks. In: Lecture notes on computer science (including subseries on lecture notes on artificial intelligence and lecture notes on bioinformatics), vol. 8485 LNCS, pp. 298–310. 2014. https://doi.org/10.1007/978-3-319-08010-9_33.

  33. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors, pp. 1–18. 2012. arXiv:1207.0580.

  34. Karabulut EM, Ibrikci T. Analysis of cardiotocogram data for fetal distress determination by decision tree based adaptive boosting approach. J Comput Commun. 2014;02:32–7. https://doi.org/10.4236/jcc.2014.29005.

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Mehdi Fasihi & Mohammad H. Nadimi-Shahraki

  2. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Mehdi Fasihi & Mohammad H. Nadimi-Shahraki

  3. Iowa State University, Ames, IA, USA

    Ali Jannesari

Authors
  1. Mehdi Fasihi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad H. Nadimi-Shahraki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Jannesari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Fasihi.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Artificial Intelligence for HealthCare” guest edited by Lydia Bouzar-Benlabiod, Stuart H. Rubin and Edwige Pissaloux.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fasihi, M., Nadimi-Shahraki, M.H. & Jannesari, A. A Shallow 1-D Convolution Neural Network for Fetal State Assessment Based on Cardiotocogram. SN COMPUT. SCI. 2, 287 (2021). https://doi.org/10.1007/s42979-021-00694-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00694-6

Keywords

Search

Navigation