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bioRxiv · 10.1101/2023.05.17.540314

Creation of predictive cardiac excitation models at the tissue scale with machine learning in augmented state space

Kabus, D.·de Vries, A. A. F.·Pijnappels, D. A.·Dierckx, H.
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Abstract

The quick and easy creation of fast in-silico models of excitable media is, for instance, needed for patient-specific predictions in diagnostics and decision making in cardiac electrophysiology. We here present a model creation pipeline that not only generates new models quickly, but also only requires data from one easily measurable spatio-temporal variable. These data may, for instance, be an optical voltage mapping recording of the electrical waves in cardiac muscle tissue controlling the heart beat. We use exponential moving averages and compute standard deviations to extract additional states from this one variable to span a sparse discretised state space. The standard deviation in a neighbourhood can be used as a proxy for the gradient. To this augmented state space, we fit a simple polynomial model to predict the evolution of this one state variable. For optical voltage mapping data of human atrial myocyte monolayers electrically stimulated by stochastic burst pacing, the data-driven model is able to describe the excitation and recovery of the system, as well as wave propagation. The data-driven model is also able to predict spiral waves only based on data from focal waves. In contrast to conventional models, with our model creation pipeline new models can be generated in a matter of hours from experiment to fitting, rather than months or years. HighlightsO_LIModels of excitation waves can be created in minutes from experiment to fitting. C_LIO_LIOne variable in space and time is sufficient to create a working excitation model. C_LIO_LIA polynomial can predict excitation waves based on useful extracted features. C_LIO_LISpiral waves in heart muscle tissue can be predicted from focal wave data. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/540314v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@2165acorg.highwire.dtl.DTLVardef@9a1bfeorg.highwire.dtl.DTLVardef@1a3cd50org.highwire.dtl.DTLVardef@fc5698_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Kabus, D., de Vries, A. A. F., Pijnappels, D. A., Dierckx, H.. 2023-05-19. Creation of predictive cardiac excitation models at the tissue scale with machine learning in augmented state space. https://doi.org/10.1101/2023.05.17.540314

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