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

Abstract : 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.
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Submitted on : Thursday, May 20, 2021 - 4:10:50 PM
Last modification on : Saturday, May 22, 2021 - 3:29:29 AM



André Guillou, Jean-Marc Sellal, Sarah Ménétré, Grégory Petitmangin, Jacques Felblinger, et al.. Adaptive step size LMS improves ECG detection during MRI at 1.5 and 3 T. Magnetic Resonance Materials in Physics, Biology and Medicine, Springer Verlag, 2017, 30 (6), pp.567-577. ⟨10.1007/s10334-017-0638-8⟩. ⟨hal-03231308⟩



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