  The realisation of precision cardiology requires novel techniques for the
non-invasive characterisation of individual patients' cardiac function to
inform therapeutic and diagnostic decision-making. The electrocardiogram (ECG)
is the most widely used clinical tool for cardiac diagnosis. Its interpretation
is, however, confounded by functional and anatomical variability in heart and
torso. In this study, we develop new computational techniques to estimate key
ventricular activation properties for individual subjects by exploiting the
synergy between non-invasive electrocardiography and image-based
torso-biventricular modelling and simulation. More precisely, we present an
efficient sequential Monte Carlo approximate Bayesian computation-based
inference method, integrated with Eikonal simulations and torso-biventricular
models constructed based on clinical cardiac magnetic resonance (CMR) imaging.
The method also includes a novel strategy to treat combined continuous
(conduction speeds) and discrete (earliest activation sites) parameter spaces,
and an efficient dynamic time warping-based ECG comparison algorithm. We
demonstrate results from our inference method on a cohort of twenty virtual
subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering
low versus high resolution for the endocardial discretisation (which determines
possible locations of the earliest activation sites). Results show that our
method can successfully infer the ventricular activation properties from
non-invasive data, with higher accuracy for earliest activation sites,
endocardial speed, and sheet (transmural) speed in sinus rhythm, rather than
the fibre or sheet-normal speeds.
