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Updated: Feb 28, 2026
Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
Published on: March 19, 2021
Adaptive step size LMS improves ECG detection during MRI at 1.5 and 3 T
André Guillou1,2, Jean-Marc Sellal2,3, Sarah Ménétré1
1Schiller SA, 4 rue Pasteur, 67160, Wissembourg, France.
Objective:
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).
Results:
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.
Conclusion:
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.
