Datenpaket: In silico electrocardiograms of 1.8 million ventricular extrasystoles and corresponding activation maps (part 3)

1.8 million ECGs derived from multiscale simulations of cardiac electrophysiology of ventricular extrasystoles. 1000 anatomical variants of a bi-ventricular mesh x 600 excitation origins x 3 heart posiitions in the torso.

This dataset contains about 1.8 million body surface potentials (BSPs) simulated using 1000 heart models generated using a statistical shape model. It has been used in [1,1a]. Here, only the noise-free BSPs are provided. Due to its size, this is a multi-part dataset. Part 1: https://doi.org/10.5445/IR/1000156139 Part 2: https://doi.org/10.5445/IR/1000156554 Part 3: https://doi.org/10.5445/IR/1000156555 Part 4: https://doi.org/10.5445/IR/1000156556 Part 5: https://doi.org/10.5445/IR/1000156557 Each archive XXXX-YYYY.tar contains 20 heart models and corresponding signals. Each subdirectory within the archive contains:

• heart.vtp: A triangle mesh of the heart including the point data:
  • ab, rt, rtCos, rtSin, tm, tv: Consistent biventricular coordinates [2].
  • class: Boundary regions used as input for the computation of fiber orientations [3].
  • trigger: 1-based indices of the ca. 600 foci (-1000 if not a focus).
• heart_transform_matrices.mat: A 1 x 3 cell array containing 4 x 4 transformation matrices that describe the pose of the heart within the torso. Apply the matrix from heart_transform_matrices.mat to the nodes in heart.vtp.
• actTimes.mat: A numNodes x numFoci matrix of activation times computed using the fast iterative method [4,5] (conduction velocity in fiber direction: 1 m/s, perpendicular to fiber direction: 1/2.7 m/s).
• bsp.mat:
  • bsp: A numElectrodes x numTimeSamples x numHeartPoses x numFoci matrix of BSPs computed by aligning a transmembrane voltage template with scaled activation times (see actTimeScalings.mat below) and solving the second bidomain equation using the boundary element method [6].
  • bspEnd: Time index of the end of depolarization (largest scaled activation time). The archive general.tar contains heart-model-independent data and parameters used to generate the individual heart models:
• torso.vtp: A triangle mesh of the torso including the point data:
  • electrodes: 1-based indices of the 200 electrodes (-1000 if not an electrode).
• heart_meanshape.vtp: A triangle mesh of the mean shape of the statistical shape model [7,8].
• heart_shapemodel.mat:
  • pc: A 3*numNodes x numModes matrix of principal components (numModes = 100).
  • var: A numModes x 1 vector of variances.
  • weights: A numModes x numModels matrix of weights used to generate the 1000 heart models.
• heart_alignment_matrix.mat: A 4 x 4 transformation matrix describing the alignment of the mean shape with the torso-specific heart. Only to be appleid to node coordinates in within general.tar (already contained in heart_transform_matrices.mat).
• heart_transform_params.mat: A struct containing roll, pitch, yaw angles and x, y, z translations used to generate the heart_transform_matrices.mat (see above).
• fiber_angles.mat:
  • alphaEndo: numModels x 1 vector of endocardial fiber angles used to generate fiber orientations.
  • alphaEpi: numModels x 1 vector of epicardial fiber angles used to generate fiber orientations.
• actTimeScalings.mat:
  • A numModels x numFoci matrix of factors used to scale the activation times.
• tmv_template.mat: The transmembrane voltage time course used to compute BSPs.
• heart_classes.vtp: A coarse triangle mesh of the mean shape used for fuzzy classification.
• heart_classes_subdiv.vtp: A subdivided version of the coarse triangle mesh of the mean shape used to convert between Cobiveco and barycentric coordinates. [1] https://doi.org/10.48550/arXiv.2209.08095 [1a]https://doi.org/10.1016/j.artmed.2023.102619 [2] https://doi.org/10.1016/j.media.2021.102247 [3] https://github.com/KIT-IBT/LDRB_Fibers [4] https://github.com/KIT-IBT/FIM_Eikonal [5] https://doi.org/10.1137/120881956 [6] https://doi.org/10.1016/j.cmpb.2007.09.004 [7] https://doi.org/10.5281/zenodo.4506463 [8] https://doi.org/10.1016/j.media.2015.08.009