Key result
An artificial neural network architecture was proposed to non-invasively reconstruct cardiac activation maps directly from body surface potentials, evaluated using simulated atrial data.
Why the study?
Activation maps are used to understand cardiac arrhythmias but traditionally require electrograms recorded invasively or non-invasively reconstructed using inverse methods.
Can artificial neural networks accurately reconstruct cardiac activation maps directly from body surface potentials?
Can artificial neural networks accurately reconstruct cardiac activation maps directly from body surface potentials?
Artificial neural networks may offer a novel non-invasive approach to reconstruct cardiac activation maps directly from body surface potentials, as demonstrated in simulated atrial data.
Hypothesis-generating for non-invasive activation mapping; leaves open human validation before any clinical consideration.
Cardiac mapping is an important diagnostic tool to understand cardiac arrhythmias caused by an electrical disorder. One of its procedures is the activation mapping which consists on generating a map of the depolarization sequence. This map is constructed from the electrograms (EGMs). These latter signals are either recorded invasively through catheterization or non-invasively reconstructed using inverse methods. Our purpose is to evaluate the performance of artificial neural networks on reconstructing activation maps non-invasively directly from body surface potentials (BSPs) without providing the electrograms. We suggest a basic architecture of a neural network. The method is evaluated using simulated atrial data.
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Karoui et al. (2019) studied Cardiac arrhythmias. Artificial neural networks was evaluated on Performance of artificial neural networks on reconstructing activation maps. An artificial neural network architecture was proposed to non-invasively reconstruct cardiac activation maps directly from body surface potentials, evaluated using simulated atrial data.
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