Unipolar Electrogram Eigenvalue Distribution Analysis for the Identification of Atrial Fibrosis

Riccio, Jennifer; Rocher, Sara; Martinez-Mateu, Laura; Alcaine, Alejandro; Sáiz, Javier; Martínez, Juan Pablo; Laguna, Pablo

doi:10.22489/cinc.2020.434

Cited by 2 publications

(2 citation statements)

References 10 publications

(12 reference statements)

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“…This performance was, however, reduced under lower levels of fibrosis (20%) if this was distributed in patches, making the error subsequently propagate to identification of ACh sites. Other strategies for fibrosis detection based on bipolar EGM amplitude or using shape-based methods (Riccio et al, 2020 ) could improve the performance in those specific cases.…”

Section: Discussionmentioning

confidence: 99%

Location of Parasympathetic Innervation Regions From Electrograms to Guide Atrial Fibrillation Ablation Therapy: An in silico Modeling Study

et al. 2021

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The autonomic nervous system (ANS) plays an essential role in the generation and maintenance of cardiac arrhythmias. The cardiac ANS can be divided into its extrinsic and intrinsic components, with the latter being organized in an epicardial neural network of interconnecting axons and clusters of autonomic ganglia called ganglionated plexi (GPs). GP ablation has been associated with a decreased risk of atrial fibrillation (AF) recurrence, but the accurate location of GPs is required for ablation to be effective. Although GP stimulation triggers both sympathetic and parasympathetic ANS branches, a predominance of parasympathetic activity has been shown. This study aims was to develop a method to locate atrial parasympathetic innervation sites based on measurements from a grid of electrograms (EGMs). Electrophysiological models representative of non-AF, paroxysmal AF (PxAF), and persistent AF (PsAF) tissues were developed. Parasympathetic effects were modeled by increasing the concentration of the neurotransmitter acetylcholine (ACh) in randomly distributed circles across the tissue. Different circle sizes of ACh and fibrosis geometries were considered, accounting for both uniform diffuse and non-uniform diffuse fibrosis. Computational simulations were performed, from which unipolar EGMs were computed in a 16 × 1 6 electrode mesh. Different distances of the electrodes to the tissue (0.5, 1, and 2 mm) and noise levels with signal-to-noise ratio (SNR) values of 0, 5, 10, 15, and 20 dB were tested. The amplitude of the atrial EGM repolarization wave was found to be representative of the presence or absence of ACh release sites, with larger positive amplitudes indicating that the electrode was placed over an ACh region. Statistical analysis was performed to identify the optimal thresholds for the identification of ACh sites. In all non-AF, PxAF, and PsAF tissues, the repolarization amplitude rendered successful identification. The algorithm performed better in the absence of fibrosis or when fibrosis was uniformly diffuse, with a mean accuracy of 0.94 in contrast with a mean accuracy of 0.89 for non-uniform diffuse fibrotic cases. The algorithm was robust against noise and worked for the tested ranges of electrode-to-tissue distance. In conclusion, the results from this study support the feasibility to locate atrial parasympathetic innervation sites from the amplitude of repolarization wave.

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Section: Discussionmentioning

confidence: 99%

Location of Parasympathetic Innervation Regions From Electrograms to Guide Atrial Fibrillation Ablation Therapy: An in silico Modeling Study

et al. 2021

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“…Four different scenarios for each u k (n) were considered in this study, as already proposed in [37], with/without fibrosis and with/without prior alignment. Their approximate theoretical eigenvalues and EIGDR were derived following parallel methodology to that used in [36] for repetitive signal ensemble alignment, as detailed below.…”

Section: Unipolar Signal Modelingmentioning

confidence: 99%

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

Riccio

Alcaine

Rocher

et al. 2022

Med Biol Eng Comput

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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 $$\mathcal {R}$$ R and $$\mathcal{R}^{\mathcal{A}}$$ R A , respectively, also mapping the gain in eigenvalue concentration obtained by the alignment, $$\Delta \mathcal{R}^{\mathcal{A}}$$ 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 $$\mathcal{R}^{\mathcal{A}}$$ 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 $$\mathcal {R}^{\mathcal {A}}$$ R A from 3×3 cliques for Ψ= 0∘ and bipolar voltage map Vb-m, performed assuming a variable electrode-to-tissue distance and noisy u-EGMs (noise level σv = 46.4 μV ). Lower panels: detected fibrotic areas (brown), using the thresholds that maximize detection accuracy of each map

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Unipolar Electrogram Eigenvalue Distribution Analysis for the Identification of Atrial Fibrosis

Cited by 2 publications

References 10 publications

Location of Parasympathetic Innervation Regions From Electrograms to Guide Atrial Fibrillation Ablation Therapy: An in silico Modeling Study

Location of Parasympathetic Innervation Regions From Electrograms to Guide Atrial Fibrillation Ablation Therapy: An in silico Modeling Study

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

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