Atrial fibrosis identification with unipolar electrogram eigenvalue distribution analysis in multi-electrode arrays

• Published in: Medical & biological engineering & computing 2022-11, Vol.60 (11), p.3091-3112
• Main Authors: Riccio, Jennifer, Alcaine, Alejandro, Rocher, Sara, Martinez-Mateu, Laura, Saiz, Javier, Invers-Rubio, Eric, Guillem, Maria S., Martínez, Juan Pablo, Laguna, Pablo
• Format: Article
• Language: English
• Subjects: Ablation; Accuracy; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Cardiac arrhythmia; Computer Applications; Eigenvalues; Electric potential; Electrodes; Fibrosis; Human Physiology; Imaging; Medical instruments; Noise levels; Original; Original Article; Panels; Radiology; Tissues; Voltage; Waveforms
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-022-02648-3

Atrial fibrosis plays a key role in the initiation and progression of atrial fibrillation (AF). Atrial fibrosis is typically identified by a peak-to-peak amplitude of bipolar electrograms (b-EGMs) lower than 0.5 mV, which may be considered as ablation targets. Nevertheless, this approach disregards signal spatiotemporal information and b-EGM sensitivity to catheter orientation. To overcome these limitations, we propose the dominant-to-remaining eigenvalue dominance ratio (EIGDR) of unipolar electrograms (u-EGMs) within neighbor electrode cliques as a waveform dispersion measure, hypothesizing that it is correlated with the presence of fibrosis. A simulated 2D tissue with a fibrosis patch was used for validation. We computed EIGDR maps from both original and time-aligned u-EGMs, denoted as R and R A , respectively, also mapping the gain in eigenvalue concentration obtained by the alignment, Δ R A . The performance of each map in detecting fibrosis was evaluated in scenarios including noise and variable electrode-tissue distance. Best results were achieved by R A , reaching 94% detection accuracy, versus the 86% of b-EGMs voltage maps. The proposed strategy was also tested in real u-EGMs from fibrotic and non-fibrotic areas over 3D electroanatomical maps, supporting the ability of the EIGDRs as fibrosis markers, encouraging further studies to confirm their translation to clinical settings. Graphical Abstract Upper panels: map of R A from 3×3 cliques for Ψ= 0 ∘ and bipolar voltage map V b - m , performed assuming a variable electrode-to-tissue distance and noisy u-EGMs (noise level σ v = 46.4 μ V ). Lower panels: detected fibrotic areas (brown), using the thresholds that maximize detection accuracy of each map