Noninvasive Identification of Atrial Fibrillation Drivers: Simulation and Patient Data Evaluation

In this study, the inverse solution with a single dipole was computed to localize the premature ventricular contraction (PVC) origin from long term multiple leads ECG measurements on fourteen patients. The stability of the obtained results was studied with respect to the preprocessing of signals used as an input to the inverse solution and the complexity of the torso model. Two methods were used for the baseline drift removal. After an averaging of the heartbeats, the influence of the retention or elimination of the remaining offset at the beginning of the PVC signal was examined. The inverse computations were performed using both homogeneous and inhomogeneous patient-specific torso models. It was shown that the remaining offset in the averaged signals at the beginning of the PVC signal had the most significant impact on the stability of the resulting position within the ventricles. Its elimination stabilizes the location of the results, decreases the sensitivity to the torso model complexity and decreases the sensitivity to the primary baseline drift removal method. The additional offset correction decreased the mean distance between the results for all patients from 17-18 mm to 1-2 mm, regardless of the baseline drift removal method or the torso model complexity.

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Noninvasive Identification of Atrial Fibrillation Drivers: Simulation and Patient Data Evaluation

Abstract: Identification of atrial drivers as singularity points by using the inverse problem of electrocardiography is being

Cited by 2 publications

References 12 publications

An expert review of the inverse problem in electrocardiographic imaging for the non-invasive identification of atrial fibrillation drivers

An expert review of the inverse problem in electrocardiographic imaging for the non-invasive identification of atrial fibrillation drivers

The Importance of ECG Offset Correction for Premature Ventricular Contraction Origin Localization from Clinical Data

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