Foetal ECG extraction using non-linear adaptive noise canceller with multiple primary channels

Foetal ECG extraction using non-linear adaptive noise canceller with multiple primary channels

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A non-linear multi-sensory adaptive noise canceller (ANC, MsANC) with both multi-primary and multi-reference channels is proposed for foetal electrocardiogram (FECG) extraction. The primary channels are connected by a linear combiner (LC) whose output serves as a primary signal for the whole MsANC. A finite impulse response (FIR) filter or a non-linear filter [a Volterra filter, or an FIR filter plus a functional link artificial neural network (FLANN), or an FIR filter plus a generalised FLANN] is placed in each reference channel to approximate the linear and non-linear mappings between the chest maternal ECG (ANC reference signal) and the abdominal ECG (ANC primary signal). The LC connecting the primary channels is updated by a constrained recursive least square algorithm, while the linear and non-linear filters placed in reference channels are updated in a least mean square sense. Two real datasets derived from the Physionet non-invasive FECG database as well as the DaISy database are used to show the effectiveness of the proposed MsANC. Experimental results have revealed that the proposed MsANC results in considerable performance improvement as the number of primary channels is increased in comparison with existing ANCs with a single primary channel.


    1. 1)
      • 1. Sameni, R., Clifford, G.D.: ‘A review of fetal ECG signal processing; issues and promising directions’, Open Pacing, Electrophysiol. Ther. J., 2010, 3, pp. 420.
    2. 2)
      • 2. Behar, J., Andreotti, F., Zaunseder, S., et al: ‘A practical guide to non-invasive foetal electrocardiogram extraction and analysis’, Physiol. Meas., 2016, 37, (5), pp. R1R35.
    3. 3)
      • 3. Andreotti, F., Behar, J., Zaunseder, S., et al: ‘An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms’, Physiol. Meas., 2016, 37, (5), pp. 627648.
    4. 4)
      • 4. Behar, J., Oster, J., Clifford, G.D.: ‘Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data’, Physiol. Meas., 2014, 35, (8), pp. 15691589.
    5. 5)
      • 5. Clifford, G.D., Silva, I., Behar, J., et al: ‘Non-invasive fetal ECG analysis’, Physiol. Meas., 2014, 35, (8), pp. 15211536.
    6. 6)
      • 6. Kanjilal, P., Palit, S., Saha, G.: ‘Fetal ECG extraction from single-channel maternal ECG using singular value decomposition’, IEEE Trans. Biomed. Eng., 1997, 44, (1), pp. 5159.
    7. 7)
      • 7. Hyvarinen, A.: ‘Fast and robust fixed-point algorithms for independent component analysis’, IEEE Trans. Neural Netw., 1999, 10, (3), pp. 626634.
    8. 8)
      • 8. Camargo-Olivares, J.L., Martin-Clemente, R., Hornillo-Mellado, S., et al: ‘The maternal abdominal ECG as input to MICA in the fetal ECG extraction problem’, IEEE Signal Process. Lett., 2011, 18, (3), pp. 161164.
    9. 9)
      • 9. Sameni, R., Jutten, C., Shamsollahi, M.B., et al: ‘A deflation procedure for subspace decomposition’, IEEE Trans. Signal Process., 2010, 58, (4), pp. 23632374.
    10. 10)
      • 10. Martens, S.M., Rabotti, C., Mischi, M., et al: ‘A robust fetal ECG detection method for abdominal recordings’, Physiol. Meas., 2007, 28, (4), pp. 373388.
    11. 11)
      • 11. Taralunga, D.D., Wolf, W., Strungaru, R., et al: ‘Abdominal fetal ECG enhancement by event synchronous canceller’. Int. Conf. IEEE Engineering in Medicine & Biology Society, Vancouver, Canada, August 2008, pp. 54025405.
    12. 12)
      • 12. Vullings, R., Peters, C.H., Sluijter, R.J., et al: ‘Dynamic segmentation and linear prediction for maternal ECG removal in antenatal abdominal recordings’, Physiol. Meas., 2009, 30, (3), pp. 291307.
    13. 13)
      • 13. Niknazar, M., Rivet, B., Jutten, C.: ‘Fetal ECG extraction by extended state Kalman filtering based on single-channel recordings’, IEEE Trans. Biomed. Eng., 2013, 60, (5), pp. 13451352.
    14. 14)
      • 14. Andreotti, F., Riedl, M., Himmelsbach, T., et al: ‘Robust fetal ECG extraction and detection from abdominal leads’, Physiol. Meas., 2014, 35, (8), pp. 15511567.
    15. 15)
      • 15. Lee, K.J., Lee, B.: ‘Sequential total variation denoising for the extraction of fetal ECG from single-channel maternal abdominal ECG’, Sensors, 2016, 16, (7), pp. 10211035.
    16. 16)
      • 16. Widrow, B., Glover, J.R., McCool, J.M., et al: ‘Adaptive noise cancelling: principles and applications’, Proc. IEEE, 1975, 63, (12), pp. 16921716.
    17. 17)
      • 17. Camps-Valls, G., Martínez-Sober, M., Soria-Olivas, E., et al: ‘Foetal ECG recovery using dynamic neural networks’, Artif. Intell. Med., 2004, 31, (3), pp. 197209.
    18. 18)
      • 18. Shadaydeh, M., Xiao, Y., Ward, R.: ‘Extraction of fetal ECG using adaptive Volterra filters’. 16th European Signal Processing Conf., Lausanne, Switzerland, August 2008, pp. 15.
    19. 19)
      • 19. Zheng, W., Liu, H., Cheng, J.: ‘Adaptive filtering in phase space for foetal electrocardiogram estimation from an abdominal electrocardiogram signal and a thoracic electrocardiogram signal’, IET Signal Process., 2012, 6, (3), pp. 171177.
    20. 20)
      • 20. Han, L., Pu, X., Chen, X.: ‘Method of fetal electrocardiogram extraction based on v-support vector regression’, IET Signal Process., 2015, 9, (5), pp. 430439.
    21. 21)
      • 21. Behar, J., Johnson, A., Clifford, G.D., et al: ‘A comparison of single channel fetal ECG extraction methods’, Ann. Biomed. Eng., 2014, 42, (6), pp. 13401353.
    22. 22)
      • 22. Ma, Y., Xiao, Y., Wei, G., et al: ‘Fetal ECG extraction using functional link artificial neural networks’. Asia-Pacific Signal and Information Processing Association Annual Summit Conf., Siem Reap, Cambodia, December 2014, pp. 14.
    23. 23)
      • 23. Ma, Y., Xiao, Y., Wei, G., et al: ‘A multichannel nonlinear adaptive noise canceller based on generalized FLANN for fetal ECG extraction’, Meas. Sci. Technol., 2016, 27, (1), pp. 1570315715.
    24. 24)
      • 24. Vorobyov, S.A., Cichocki, A., Bodyanskiy, Y.: ‘Adaptive noise cancellation for multi-sensory signals’, Fluct. Noise Lett., 2001, 1, (1), pp. 112.
    25. 25)
      • 25. Goldberger, A.L., Amaral, L.A., Glass, L., et al: ‘Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals’, Circulation, 2000, 101, (23), pp. 215220.
    26. 26)
      • 26. De Moor, B. (Ed.): ‘DaISy: database for the identification of systems’, 1997. Available at
    27. 27)
      • 27. Sameni, R., Vrins, F., Parmentier, F., et al: ‘Electrode selection for noninvasive fetal electrocardiogram extraction using mutual information criteria’. AIP Conf. Proc., Paris, France, July 2006, pp. 97104.
    28. 28)
      • 28. Patra, J.C., Pal, R.N.: ‘A functional link artificial neural network for adaptive channel equalization’, Signal Process., 1995, 43, (2), pp. 181195.
    29. 29)
      • 29. Sicuranza, G.L., Carini, A.: ‘A generalized FLANN filter for nonlinear active noise control’, IEEE Trans. Audio Speech Lang. Process., 2011, 19, (8), pp. 24122417.
    30. 30)
      • 30. Pan, J., Tompkins, W.J.: ‘A real-time QRS detection algorithm’, IEEE Trans. Biomed. Eng., 1985, 32, (3), pp. 230236.
    31. 31)
      • 31. Yadav, S.K., Sinha, R., Bora, P.K.: ‘Electrocardiogram signal denoising using non-local wavelet transform domain filtering’, IET Signal Process., 2015, 9, (1), pp. 8896.

Related content

This is a required field
Please enter a valid email address