Validation of a Customized Method for Estimating Electrical Potentials in the Torso From Atrial Signals: a Computational-Clinical Study

Abstract

Atrial fibrillation (AF) is a common supraventricular arrhythmia (SVA) in clinical practice and is characterized by uncoordinated electrical activity of the atria. This study aims to evaluate the influence on the forward solution of AF torso biomarkers under different levels of noise, 3D cardiorespiratory torso/atria morphologies, and number of atria electrodes. 2,048 atrial epicardium electrograms (AEGs) from 5 AF mathematical models were used to estimate 771 body surface potentials (BSPs). The BSPs and respective frequency/phase maps of are obtained after: (i) introduction of noise in the AEGs, (ii) 3D geometry torso/atria modification, and (iii) reduction in electrodes (from 2,048 to 256, 128, 64 e 32; interpolation methods: Linear/Laplacian). To reduce biomarkers disparity, a Butterworth bandpass filter (BPF) at different cut-off frequencies (0.5-30, 3–30 and HDF±1 Hz) is applied on the AEGs prior BSPs estimation. The above methodology is extended to two AF patients (EDGAR database). The estimation of AF BSPs, in different noise ranges, limits the effectiveness of the forward solution. Phase biomarkers are sensitive to the AEGs' pre-processing strategy. The BPF around HDF showed the best agreement between the different SNR levels. Due to the 3D morphological changes, HDF areas variability increased.