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A Mixed Approach for Fetal QRS Complex Detection

  • Lijuan Liao
  • Wei Zhong
  • Xuemei Guo
  • Guoli Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

Non-invasive fetal electrocardiogram (NI-FECG) plays an important role in detecting and diagnosing fetal diseases. Fetal electrocardiogram (FECG) is used to know the information of the fetal health. In this paper, we propose a mixed approach for extracting FECG from maternal abdominal ECG (AECG) recording. The proposed method is based on a combination of the wavelet transform and Support Vector Machines (SVM). As a first tier, the wavelet transform is used to detect maternal QRS complex from abdominal ECG recording. Then, a coherent averaging method was using to construct MECG and remove MECG from AECG recording. After removing MECG, SVM is used to locate fetal QRA complex from residual signal. The accuracy (84.53%) and Positive predictive value (PPV) (89.6%) in this study are much higher than other method.

Keywords

NI-FECG Wavelet transform SVM Signal quality assessment PPV 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of P. R. China under Grant No. 61375080, and the Key Program of Natural Science Foundation of Guangdong, China under Grant No. 2015A030311049. The Guangzhou science and technology project under Grant Nos. 201510010017, 201604010101.

References

  1. 1.
    H.-B. Li, S.-Y. Fang, Development of internet-based home telemonitoring system for fetus. Chin. Med. Equip. J. 2, 17–19 (2006)Google Scholar
  2. 2.
    N. Ivanushkina, K. Ivanko E. Lysenko, et al., Fetal Electrocardiogram Extraction from Maternal Abdominal Signals (Kyiv, 2014) pp. 334–338Google Scholar
  3. 3.
    S.B. Barnett, D. Maulik, Guidelines and recommendations for safe use of doppler ultrasound in perinatal applications. J. Matern. Fetal Med. 10(2), 75–84 (2001)CrossRefGoogle Scholar
  4. 4.
    M. Sato, Y. Kimura, S. Chida et al., A novel extraction method of fetal electrocardiogram from the composite abdominal signal. IEEE Trans. Biomed. Eng. 1, 49–58 (2007)CrossRefGoogle Scholar
  5. 5.
    F. Andreotti, J. Behar, S. Zaunseder, J. Oster, G.D. Clifford, An open-source framework for stress-testing non-invasive foetal ecg extraction algorithms. Physiol. Meas. 37(5), 627 (2016)CrossRefGoogle Scholar
  6. 6.
    B. Widrow, J.R. Glover, J. McCool, J. Kaunitz, C. Williams, R. Hearn, J. Zeidler, J. Eugene Dong, R. Goodlin, Adaptive noise cancelling: principles and applications. Proc. IEEE 63, 1692–1696 (1975)CrossRefGoogle Scholar
  7. 7.
    J. Behar, A. Johnson, G.D. Clifford, J. Oster, A comparison of single channel foetal ECG extraction methods Ann. Biomed. Eng. 42, 1340–1353 (2014)Google Scholar
  8. 8.
    R. Bhoker, J.P Gawande, Fetal ECG extraction using wavelet transform. ITSI Trans. Electr. Electron. Eng. (1), 19–22 (2013)Google Scholar
  9. 9.
    M. Akay, E. Mulder, Examining fetal heart-rate variability using matching pursuits. IEEE Eng. Med. Biol. 15, 64–72 (1996)Google Scholar
  10. 10.
    C.J. James, C.W. Hesse, Independent component analysis for biomedical signals. Physiol. Meas. 26, 15–39 (2005)CrossRefGoogle Scholar
  11. 11.
    C. Di Maria, W.F. Duan, M. Bojarnejad, F. Pan, S. King, D.C. Zheng, A. Murray, P. Langley, An algorithm for the analysis of foetal ECGs from 4-channel non-invasive abdominal recordings. Proc. Comput. Cardiol 4, 305–308 (2013)Google Scholar
  12. 12.
    P.P. Kanjilal, S. Palit, G. Saha, Fetal ECG extraction from single-channel maternal ECG using singular value decomposition. IEEE Trans. Biomed 44 51–3 (1997)Google Scholar
  13. 13.
    R. Sameni, Extraction of fetal cardiac signals from an array of maternal abdominal recordings. Ph.D. Thesis, Sharif University of Technology—Institute National Polytechnique deGrenoble, 2008, www.sameni.info/Publications/Thesis/PhDThesis.pdf
  14. 14.
    J. Behar, J. Oster, G.D. Clifford, Combining and comparing benchmarking methods of foetal ECG extraction without maternal or scalp electrode data. Physiol. Meas. 35, 1569–89 (2014)Google Scholar
  15. 15.
    P. Podziemski, J. Gierałtowski, Fetal heart rate discovery: algorithm for detection of fetal heart rate from noisy. Comput. Cardiol. 40, 333–336 (2013)Google Scholar
  16. 16.
    J. Behar, J. Oster, G.D. Clifford, Non-invasive FECG extraction from a set of abdominal sensors. Comput. Cardiol. 297–300 (2013)Google Scholar
  17. 17.
    P. Quan, D. Zhang, D. Guanzhong, Z. Hongcai, Two denoising methods by wavelet transform. IEEE Trans. Signal Proces. 47, 3401–6 (1999)Google Scholar
  18. 18.
    C. Liu, P. Li, C. Di Maria, L. Zhao, H. Zhang, Z. Chen, A multi-step method with signal quality assessment and fine-tuning procedure to locate maternal and fetal QRS complexes from abdominal ECG recordings. Physiol. Meas. 35, 1665–1683 (2014)CrossRefGoogle Scholar
  19. 19.
    C.Y. Liu, P. Li, L.N. Zhao, F.F. Liu, R.X. Wang, Real-time signal quality assessment for ECGs collected using mobile phones. Proc. Comput. Cardiol. 38 357–60 (2011)Google Scholar
  20. 20.
    S. Banerjee, R. Gupta, M. Mitra, Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement 45, 474–487 (2012)CrossRefGoogle Scholar
  21. 21.
    R. Kahankova, R. Martinek.et al. Fetal ECG Extraction from Abdominal ECG Using RLS based Adaptive Algorithms, in 2017 18th International Carpathian Control Conference (ICCC) (IEEE Conferences, 2017)Google Scholar
  22. 22.
    W. Zhong, L. Liao, X. Guo, G. Wang, A deep learning approach for fetal QRS complex detection. Physiol. Meas. (9), 045004 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Lijuan Liao
    • 1
  • Wei Zhong
    • 2
  • Xuemei Guo
    • 2
    • 3
  • Guoli Wang
    • 2
    • 3
  1. 1.School of Electronics and Information TechnologySun Yat-Sen UniversityGuangzhouChina
  2. 2.School of Data and Computer ScienceSun Yat-Sen UniversityGuangzhouPeople’s Republic of China
  3. 3.Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of EducationBeijingPeople’s Republic of China

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