Annals of Biomedical Engineering

, Volume 42, Issue 6, pp 1340–1353 | Cite as

A Comparison of Single Channel Fetal ECG Extraction Methods

  • Joachim Behar
  • Alistair Johnson
  • Gari D. Clifford
  • Julien Oster
Article

Abstract

The abdominal electrocardiogram (ECG) provides a non-invasive method for monitoring the fetal cardiac activity in pregnant women. However, the temporal and frequency overlap between the fetal ECG (FECG), the maternal ECG (MECG) and noise results in a challenging source separation problem. This work seeks to compare temporal extraction methods for extracting the fetal signal and estimating fetal heart rate. A novel method for MECG cancelation using an echo state neural network (ESN) based filtering approach was compared with the least mean square (LMS), the recursive least square (RLS) adaptive filter and template subtraction (TS) techniques. Analysis was performed using real signals from two databases composing a total of 4 h 22 min of data from nine pregnant women with 37,452 reference fetal beats. The effects of preprocessing the signals was empirically evaluated. The results demonstrate that the ESN based algorithm performs best on the test data with an F1 measure of 90.2% as compared to the LMS (87.9%), RLS (88.2%) and the TS (89.3%) techniques. Results suggest that a higher baseline wander high pass cut-off frequency than traditionally used for FECG analysis significantly increases performance for all evaluated methods. Open source code for the benchmark methods are made available to allow comparison and reproducibility on the public domain data.

Keywords

Fetal ECG Reservoir computing Template subtraction Adaptive noise canceller 

References

  1. 1.
    ANSI/AAMI/ISO EC57 (1998/(R)2008) Testing and reporting performance results of cardiac rhythm and ST-segment measurement algorithms.Google Scholar
  2. 2.
    Åström, K. J., and B. Wittenmark. Adaptive Control, 2nd ed. Reading, MA: Addison Wesley, 1994.Google Scholar
  3. 3.
    Barnett, S., and D. Maulik. Guidelines and recommendations for safe use of Doppler ultrasound in perinatal applications. J. Matern-Fetal Neonatal Med. 10(2):75–84, 2001.CrossRefGoogle Scholar
  4. 4.
    Behar, J., A. Johnson, J. Oster, and G. D. Clifford. An Echo State Neural Network for Foetal Electrocardiogram Extraction Optimised by Random Search. Nevada: NIPS Lake Tahoe, 2013a.Google Scholar
  5. 5.
    Behar, J., J. Oster, and G. D. Clifford. Non invasive FECG extraction from a set of abdominal sensors. In: Computing in Cardiology 2013, Zaragoza, Spain, 2013b.Google Scholar
  6. 6.
    Bergstra, J., and Y. Bengio. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13:281–305, 2012.Google Scholar
  7. 7.
    Cardoso, J., and A. Souloumiac. Blind beamforming for non Gaussian signals. In: Institute Electrical Engineer Proceedings of Radar Signal Processing, London, vol. 140(6), pp. 362–370, 1993.Google Scholar
  8. 8.
    Cerutti, S., G. Baselli, S. Civardi, E. Ferrazzi, A. Marconi, M. Pagani, and G. Pardi. Variability analysis of fetal heart rate signals as obtained from abdominal electrocardiographic recordings. J. Perinat Med. 14(6):445–452, 1986.CrossRefGoogle Scholar
  9. 9.
    Chatzis, S., and Y. Demiris. The copula echo state network. Pattern Recogn. 45(1):570–577, 2012.CrossRefGoogle Scholar
  10. 10.
    Clifford, G. D., J. Behar, J. Oster, and A. Johnson. IPM Open Source Code. https://physionet.org/users/gari@alum.mit.edu/works/IPMCode/, 2014. Last updated Feb 2014.
  11. 11.
    Clifford, G. D., J. Behar, Q. Li, and I. Rezek. Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiol. Meas. 33(9):1419–1433, 2012.PubMedCrossRefGoogle Scholar
  12. 12.
    Cohen, W. R., S. Ommani, S. Hassan, F. G. Mirza, M. Solomon, R. Brown, B. S. Schifrin, J. M. Himsworth, and B. R. Hayes-Gill. Accuracy and reliability of fetal heart rate monitoring using maternal abdominal surface electrodes. Acta Obstet .Gynecol. Scand. 91(11):1306–1313, 2012.PubMedCrossRefGoogle Scholar
  13. 13.
    Douglas, S. Numerically-robust O (N(2)) RLS algorithms using least-squares prewhitening. In: Proceedings of IEEE International Conference on Acoustic, Speech, and Signal Processing, vol. 1, pp. 412–415, 2000.Google Scholar
  14. 14.
    Goldberger, A. L., L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):215–220, 2000.CrossRefGoogle Scholar
  15. 15.
    Guerrero-Martinez, J., M. Martinez-Sober, M. Bataller-Mompean, and J. Magdalena-Benedito. New algorithm for fetal QRS detection in surface abdominal records. In: Computation in Cardiology, pp. 441–444, 2006.Google Scholar
  16. 16.
    Hyvärinen, A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw 10(3):626–634, 1999.CrossRefGoogle Scholar
  17. 17.
    Jaeger, H. The “echo state” approach to analysing and training recurrent neural networks. Technical Report GMD Report 148. German National Research Center for Information Technology 148, 2001. http://www.faculty.jacobs-university.de/hjaeger/pubs/EchoStatesTechRep.pdf.
  18. 18.
    Jaeger, H. A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach, 3rd revision. GMD-Forschungszentrum Informationstechnik, 2008. http://www.pdx.edu/sites/www.pdx.edu.sysc/files/Jaeger_TrainingRNNsTutorial.2005.pdf.
  19. 19.
    Kanjilal, P., S. Palit, and G. Saha. Fetal ECG extraction from single-channel maternal ECG using singular value decomposition. IEEE Trans. Biomed. Eng. 44(1):51–59, 1997.CrossRefGoogle Scholar
  20. 20.
    Kotas, M., J. Jezewski, A. Matonia, and T. Kupka. Towards noise immune detection of fetal QRS complexes. Comput. Methods Programs Biomed. 97(3):241–256, 2010.CrossRefGoogle Scholar
  21. 21.
    Lewis, M. Review of electromagnetic source investigations of the fetal heart. Med. Eng. Phys. 25:801–810, 2003.PubMedCrossRefGoogle Scholar
  22. 22.
    Lukoševičius, M. A practical guide to applying echo state networks. Lect. Notes Comput. Sci. 7700:659–686, 2012.CrossRefGoogle Scholar
  23. 23.
    Lukoševičius, M., and H. Jaeger. Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3):127–149, 2009.CrossRefGoogle Scholar
  24. 24.
    Martens, S., C. Rabotti, M. Mischi, and R. Sluijter. A robust fetal ECG detection method for abdominal recordings. Physiol. Meas. 28:373–388, 2007.PubMedCrossRefGoogle Scholar
  25. 25.
    Oudijk, M., A. Kwee, G. Visser, S. Blad, E. Meijboom, and K. Rosén. The effects of intrapartum hypoxia on the fetal QT interval. BJOG 111(7):656–660, 2004.PubMedCrossRefGoogle Scholar
  26. 26.
    Pan, J., and W. Tompkins. A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3):230–236, 1985.CrossRefGoogle Scholar
  27. 27.
    Petrenas, A., V. Marozas, L. Sornmo, and A. Lukoševičius. An echo state neural network for QRST cancelation during atrial fibrillation. IEEE Trans. Biomed. Eng. 59(10):2950–2957, 2012.CrossRefGoogle Scholar
  28. 28.
    Rodan, A., and P. Tino. Minimum complexity echo state network. IEEE Trans. Neural Netw. 22(1):131–144, 2011.CrossRefGoogle Scholar
  29. 29.
    Sameni, R. Extraction of Fetal Cardiac Signals from an Array of Maternal Abdominal Recordings. Ph.D. Thesis, Sharif University of Technology, Institut National Polytechnique de Grenoble, 2008. http://www.sameni.info/Publications/Thesis/PhDThesis.pdf.
  30. 30.
    Sameni, R., and G. D. Clifford. A review of fetal ECG signal processing; issues and promising directions. Open Pacing Electrophysiol. Ther. J. 3:4–20, 2010.PubMedCentralPubMedGoogle Scholar
  31. 31.
    Sameni, R., C. Jutten, and M. Shamsollahi. Multichannel electrocardiogram decomposition using periodic component analysis. IEEE Trans. Biomed. Eng. 55(8):1935–1940, 2008.CrossRefGoogle Scholar
  32. 32.
    Silva, I., J. Behar, G. D. Clifford, and G. B. Moody. Noninvasive fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013. In: Computing in Cardiology 2013, Zaragoza, Spain, 2013.Google Scholar
  33. 33.
    Ungureanu, M., J. Bergmans, S. Oei, and R. Strungaru. Fetal ECG extraction during labor using an adaptive maternal beat subtraction technique. Biomedizinische Technik 52(1):56–60, 2007.PubMedCrossRefGoogle Scholar
  34. 34.
    Vahidi, A., A. Stefanopoulou, and H. Peng. Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Vehicle Syst. Dyn. 43(1):31–55, 2005.CrossRefGoogle Scholar
  35. 35.
    Vullings, R., C. Peters, R. Sluijter, M. Mischi, S. Oei, and J. Bergmans. Dynamic segmentation and linear prediction for maternal ECG removal in antenatal abdominal recordings. Physiol. Meas. 30(3):291–307, 2009.PubMedCrossRefGoogle Scholar
  36. 36.
    Widrow, B., J. Glover, J. McCool, J. Kaunitz, C. Williams, R. Hearn, J. Zeidler, E. Dong, and R. Goodlin. Adaptive noise cancelling: principles and applications. Proc. IEEE 63(12):1692–1716, 1975.CrossRefGoogle Scholar
  37. 37.
    Zaunseder, S., F. Andreotti, M. Cruz, H. Stepan, C. Schmieder, H. Malberg, and A. Jank. (Como, July 2012) Fetal QRS detection by means of Kalman filtering and using the Event Synchronous Canceller. In: 7th International Workshop on Biosignal Interpretation.Google Scholar

Copyright information

© Biomedical Engineering Society 2014

Authors and Affiliations

  • Joachim Behar
    • 1
  • Alistair Johnson
    • 1
  • Gari D. Clifford
    • 1
  • Julien Oster
    • 1
  1. 1.Intelligent Patient Monitoring Group, Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK

Personalised recommendations