Adaptive step size LMS improves ECG detection during MRI at 1.5 T and 3 T - 05/01/18
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Résumé |
Object |
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 1.5T and 3T) 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 (Fig. 1).
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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Vol 10 - N° 1
P. 117-118 - janvier 2018 Retour au numéro
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