Aims: this study aims evaluating the influence on the forward solution AF torso biomarkers under atrial signals noise, morphological torso and atria alteration, and number of atria electrodes.
Methods: 2,048 atrial epicardium electrograms (AEGs) from 5 AF mathematical models are used to estimate 771 body surface potentials (BSPs) through the Boundary Element Method. The BSPs and respective frequency/phase maps of are obtained after: (i) introduction of noise in the AEGs (White Gaussian noise, SNR: -3 to 60 dB), (ii) 3D geometry torso/atria modification according to the phases of the cardiorespiratory cycle, and (iii) reduction in electrodes (from 2,048 to 256, 128, 64 e 32; interpolation methods: Linear/Laplacian). Then, from these generated maps, 15 biomarkers are calculated. 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 was extended to two AF patients (EDGAR database). Torso/atria geometries were segmented using SEG3D, and meshes obtained with Cleaver.
Results: 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. Furthermore, phase biomarkers showed to be more sensitive reducing number of electrodes/interpolation, for example filaments/s (771 leads: 6.4±1.1; 128 leads/Laplacian: 1.1± 0.6; 128 leads/Linear: 2.3±1.5). The patient data shows filaments with short durations and spread across the torso, similar to models.
Conclusion: A narrow BPF and if needed, the Laplace interpolation, should be applied prior estimation of torso’s signals. Moreover, the torso and atria 3D variants morphologies from the cardiorespiratory cycle, should be taken into account for AF forward solution studies.