Adaptive step size LMS improves ECG detection during MRI at 1.5 and 3 T

  • André Guillou
  • Jean-Marc Sellal
  • Sarah Ménétré
  • Grégory Petitmangin
  • Jacques Felblinger
  • Laurent Bonnemains
Research Article



We describe a new real-time filter to reduce artefacts on electrocardiogram (ECG) due to magnetic field gradients during MRI. The proposed filter is a least mean square (LMS) filter able to continuously adapt its step size according to the gradient signal of the ongoing MRI acquisition.

Materials and methods

We implemented this filter and compared it, within two databases (at 1.5 and 3 T) with over 6000 QRS complexes, to five real-time filtering strategies (no filter, low pass filter, standard LMS, and two other filters optimized within the databases: optimized LMS, and optimized Kalman filter).


The energy of the remaining noise was significantly reduced (26 vs. 68%, p < 0.001) with the new filter vs. standard LMS. The detection error of our ventricular complex (QRS) detector was: 11% with our method vs. 25% with raw ECG, 35% with low pass filter, 17% with standard LMS, 12% with optimized Kalman filter, and 11% with optimized LMS filter.


The adaptive step size LMS improves ECG denoising during MRI. QRS detection has the same F1 score with this filter than with filters optimized within the database.


Magnetic field gradient Electric artefact Noise reduction 



Julien Oster contributed to this paper as scientific advisor. Several authors were employed by Schiller SA when this study was designed and conducted.

Compliance with ethical standards

Conflict of interest

Three authors André Guillou, Sarah Ménétré and Grégory Petitmangin are employed by Schiller Medicals, Wissembourg, France and declare this financial link as a potential conflict of interest because the algorithm presented in the manuscript is actually implemented in two schiller commercial devices. The authors Jean-Marc Sellal, Jacques Felblinger and Laurent Bonnemains declare that they have no conflict of interest.

Ethical approval

The study complied with the Declaration of Helsinki regarding medical research on human subjects and was approved by a local ethics committee.

Informed consent

All subjects included in the study gave their informed written consent.


  1. 1.
    Felblinger J, Lehmann C, Boesch C (1994) Electrocardiogram acquisition during MR examinations for patient monitoring and sequence triggering. Magn Reson Med 32:523–529CrossRefPubMedGoogle Scholar
  2. 2.
    Felblinger J, Slotboom J, Kreis R, Jung B, Boesch C (1999) Restoration of electrophysiological signals distorted by inductive effects of magnetic field gradients during MR sequences. Magn Reson Med 41:715–721CrossRefPubMedGoogle Scholar
  3. 3.
    Oster J, Clifford G (2017) Acquisition of electrocardiogram signals during magnetic resonance imaging. Physiol Meas. doi: 10.1088/1361-6579/aa6e8c Google Scholar
  4. 4.
    Fischer SE, Wickline SA, Lorenz CH (1999) Novel real-time R-wave detection algorithm based on the vectorcardiogram for accurate gated magnetic resonance acquisitions. Magn Reson Med 42:361–370CrossRefPubMedGoogle Scholar
  5. 5.
    Chia JM, Fischer SE, Wickline SA, Lorenz CH (2000) Performance of QRS detection for cardiac magnetic resonance imaging with a novel vectorcardiographic triggering method. J Magn Reson Imaging 12:678–688CrossRefPubMedGoogle Scholar
  6. 6.
    Oster J, Pietquin O, Abacherli R, Kraemer M, Felblinger J (2009) A specific QRS detector for electrocardiography during MRI: using wavelets and local regularity characterization. 2009 IEEE international conference on acoustics, speech and signal process, IEEE, pp 341–344Google Scholar
  7. 7.
    AlMahamdy M, Riley HB (2014) Performance study of different denoising methods for ECG signals. Proced Comput Sci 37:325–332CrossRefGoogle Scholar
  8. 8.
    Oster J, Pietquin O, Abächerli R, Kraemer M, Felblinger J (2009) Independent component analysis-based artefact reduction: application to the electrocardiogram for improved magnetic resonance imaging triggering. Physiol Meas 30:1381–1397CrossRefPubMedGoogle Scholar
  9. 9.
    Oster J, Pietquin O, Kraemer M, Felblinger J (2010) Nonlinear bayesian filtering for denoising of electrocardiograms acquired in a magnetic resonance environment. IEEE Trans Biomed Eng 57:1628–1638CrossRefPubMedGoogle Scholar
  10. 10.
    Schmidt M, Krug JW, Rose G (2016) Reducing of gradient induced artifacts on the ECG signal during MRI examinations using Wilcoxon filter. Curr Dir Biomed Eng 2:175–178Google Scholar
  11. 11.
    Abächerli R, Pasquier C, Odille F, Kraemer M, Schmid J-J, Felblinger J (2005) Suppression of MR gradient artefacts on electrophysiological signals based on an adaptive real-time filter with LMS coefficient updates. Magma N Y N 18:41–50CrossRefGoogle Scholar
  12. 12.
    Wu V, Barbash IM, Ratnayaka K, Saikus CE, Sonmez M, Kocaturk O, Lederman RJ, Faranesh AZ (2011) Adaptive noise cancellation to suppress electrocardiography artifacts during real-time interventional MRI. J Magn Reson Imaging 33:1184–1193CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Tse ZTH, Dumoulin CL, Clifford GD, Schweitzer J, Qin L, Oster J, Jerosch-Herold M, Kwong RY, Michaud G, Stevenson WG, Schmidt EJ (2014) A 1.5 T MRI-conditional 12-lead electrocardiogram for MRI and intra-MR intervention: 12-lead ECG for MRI and intra-MR intervention. Magn Reson Med 71:1336–1347CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Odille F, Pasquier C, Abächerli R, Vuissoz P-A, Zientara GP, Felblinger J (2007) Noise cancellation signal processing method and computer system for improved real-time electrocardiogram artifact correction during MRI data acquisition. IEEE Trans Biomed Eng 54:630–640CrossRefPubMedGoogle Scholar
  15. 15.
    Haykin SO (2014) Adaptive filter theory, 5th edn. Pearson, LondonGoogle Scholar
  16. 16.
    Widrow B, Stearns PN (1985) Adaptive signal processing, 1st edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  17. 17.
    R Development Core Team (2009) R: a language and environment for statistical comput ing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  18. 18.
    Ozen A (2011) A novel variable step size adjustment method based on channel output autocorrelation for the LMS training algorithm. Int J Commun Syst 24:938–949CrossRefGoogle Scholar
  19. 19.
    Vega LR, Rey H, Benesty J, Tressens S (2008) A new robust variable step-size NLMS algorithm. IEEE Trans Signal Process 56:1878–1893CrossRefGoogle Scholar
  20. 20.
    Pillet N, Ayachi M, Frick V, Berviller H, Felblinger J, Blondé JP (2010) A complete device dedicated to ECG signal measurement with integrated 3D Hall sensor for signal correction. 2010 17th IEEE international conference on electronics, circuits and systems, ICECS, pp 227–230Google Scholar

Copyright information

© ESMRMB 2017

Authors and Affiliations

  1. 1.Schiller SAWissembourgFrance
  2. 2.INSERM, U947Vandoeuvre-les-NancyFrance
  3. 3.Department of CardiologyCHU NancyVandoeuvre-les-NancyFrance
  4. 4.CHU NancyVandoeuvre-les-NancyFrance
  5. 5.Department of Medical ImagingCHU NancyVandoeuvre-les-NancyFrance
  6. 6.Department of Cardiac surgeryCHU StrasbourgStrasbourgFrance
  7. 7.University of StrasbourgStrasbourgFrance

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