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

[EN] 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, Delta 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. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreements No. 766082 and No. 860974, by projects PID2019-105674RBI00, PID2019-104881RB-I00 (MICINN) and Aragon Government (Reference Group Biomedical Signal Interpretation and Computational Simulation (BSICoS) T39-20R) cofunded by FEDER 20142020 "Building Europe from Aragon", by fellowship ACIF/2018/174 and Grant PROMETEO/2020/043, both from Direccion General de Politica Cientifica de la Generalitat Valenciana, and by DENIS Project (Volunteer Computer platform) supported through CoMBA 2021-2022 internal projects call from Universidad San Jorge. Riccio, J.; Alcaine, A.; Rocher, S.; Martínez-Mateu, L.; Saiz Rodríguez, FJ.; Invers-Rubio, E.; Guillem Sánchez, MS... (2022). Atrial fibrosis identification with unipolar electrogram eigenvalue distribution analysis in multi-electrode arrays. Medical & Biological Engineering & Computing.