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Detection and Termination of Broken-Spiral-Waves in Mathematical Models for Cardiac Tissue: A Deep-Learning Approach

Mulimani, MK and Kumar Alageshan, J and Pandit, R (2019) Detection and Termination of Broken-Spiral-Waves in Mathematical Models for Cardiac Tissue: A Deep-Learning Approach. In: 2019 Computing in Cardiology, CinC 2019, 8-11 September 2019, Singapore.

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Official URL: https://doi.org/10.23919/CinC49843.2019.9005822


Defibrillation, the elimination of pathological waves of electrical activation in cardiac tissue, plays an important role in the elimination of life-threatening cardiac arrhythmias like ventricular tachycardia (VT) and ventricular fibrillation (VF). We develop a deep-learning method, which uses a convolution neural network (CNN), to develop a new defibrillation scheme applicable in 2D tisue. We begin by training our CNN with a huge dataset of spiral waves (S) and non-spiral waves (NS) that we obtain from our direct numerical simulations (DNSs) of a variety of mathematical models for the propagation of electrical waves of activation in cardiac tissue. Our trained CNN can distinguish between S NS patterns; in particular, it also detects a broken spiral wave as S. We demonstrate how to use our CNN to develop a heat map, from a broken-spiral-wave image, that yields the approximate locations of these spiral cores. We develop a defibrillation scheme that applies current, with two-dimensional (2D) Gaussian profiles of standard deviation (�), centred at square lattice sites (NG � NG) imposed on the simulation domain (N �N); the amplitudes of these Gaussians are taken from the heatmap. We explore the dependence of our Gaussian defibrillation scheme on a noisy image, which closely mimics the noisy optical image data.

Item Type: Conference Paper
Publication: Computing in Cardiology
Publisher: IEEE Computer Society
Additional Information: cited By 0; Conference of 2019 Computing in Cardiology, CinC 2019 ; Conference Date: 8 September 2019 Through 11 September 2019; Conference Code:158032
Keywords: Backpropagation; Cardiology; Chemical activation; Geometrical optics; Heart; Learning systems; Tissue, Cardiac arrhythmia; Convolution neural network; Electrical activation; Gaussian profiles; Standard deviation; Two Dimensional (2 D); Ventricular fibrillation; Ventricular tachycardia, Deep learning
Department/Centre: Division of Physical & Mathematical Sciences > Physics
Date Deposited: 04 Sep 2020 05:55
Last Modified: 28 Aug 2022 07:43
URI: https://eprints.iisc.ac.in/id/eprint/64913

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