Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset

Figures

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  Figure 1. (A) Dimensions of the in silico setup. Tissue dimensions, catheter position, and fibrotic dimensions are shown in the left panel. In the top right corner, bath dimensions are depicted. A cross-section cut showing the interstitial model is depicted in the lower right corner. (B) Isometric view of the two setups used for the in silico experiments. The left panel shows the setup using the HD Grid catheter (St. Jude Medical, EnSite HD Grid catheter, St. Paul, MN) and the fibrotic tissue. The right panel shows the setup using the Lasso catheter (Biosense Webster, Diamond Bar, CA, USA) on top of the tissue. Reused from Sánchez et al. (2021).

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  Figure 2. Workflow to generate the noise model and the addition to the simulated signals. In the top left corner, the different segments of the activity from a clinical unipolar electrogram are depicted. Autoregression was applied to the noise segments. The noise model was used to estimate the simulated unipolar electrogram with noise. Afterward, the unipolar electrograms (red and blue trace) were filtered, and the bipolar electrogram was calculated by subtracting the unipolar electrograms. Reused from Sánchez et al. (2021).

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  Figure 3. Electrogram activity detection in the Hilbert space. (A) Electrogram signal (C) in the Hilbert Space with centroid (orange trace), green arrow depicts the distance measured from the centroid to the signal. (B) Frequency distribution of centroid to signal distance, red line represents mean value plus one standard deviation. (C) Bipolar electrogram (blue trace) and activity segments (orange trace).

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  Figure 4. Importance of using a realistic electrode geometry and adding noise for simulated intracardiac signals. (A) Bipolar electrogram signal recorded with a cubic electrode (blue trace) and the corresponding filtered signal (red trace). (B) Signal recorded with a cylindrical electrode (blue trace) and the resulting signal after filtering (red trace). (C) Simulated signals recorded with a cylindrical electrode with and without noise and the resulting signals after filtering. (D) Comparison of a simulated signal with a clinical signal. (E) Electrograms recorded on the surface of the fibrotic tissue with different densities. (F) Effect of fibrosis growth from the endocardial surface to the epicardium (0.5, 1, 2 mm) on the electrogram (60% fibrosis density). Reused from Sánchez et al. (2021).

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  Figure 5. Feature distribution for all in vivo and in silico electrograms (including noise). Single feature distribution can be observed in the diagonal and the combination of two features is reflected in the scatter plots. (A) Features split by different densities of fibrosis. (B) Features split by different degrees of transmurality. Duration, duration of the active segment (ms); SmpEn, sample entropy; ShEn, shannon entropy; SpEn, spectral entropy; p2p, peak-to-peak amplitude (mV); Kolmogorov, Kolmogorov complexity; Fractal, fractal dimension. Reused from Sánchez et al. (2021).

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  Figure 6. (A) Confusion matrix of the decision tree classifier for identifying non-fibrotic vs. fibrotic substrate. (B) Confusion matrix showing the performance for identifying different fibrosis densities. (C) Confusion matrix showing the performance for identifying transmurality of fibrosis. (D) Effect of increasing the electrode surface to tissue surface distance on the accuracy of the classifiers to distinguish fibrotic tissue, density, and transmurality. Reused from Sánchez et al. (2021).

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  Figure 7. Anterior and posterior view of patient maps for clinical low/high voltage (A) and classification results for non-fibrotic vs. fibrotic (B), fibrosis density (C), and fibrosis transmurality (D). The green dot represents a signal at the base of the pulmonary vein which was marked as high voltage and classified as subendocardial (0.5 mm) low density (10%) fibrotic tissue. The white dot refers to a signal recorded in the pulmonary vein classified as low voltage and high density (60%), transmural (2 mm) fibrotic tissue. The yellow dot represents a high voltage area identified as non-fibrotic and the light blue dot indicates a signal collected in the pulmonary vein annotated as high voltage and classified as low density (20%), partially transmural (1 mm) fibrotic tissue. Reused from Sánchez et al. (2021).