A statistical approach to incorporate multiple ECG or EEG recordings with artifactual variability into inverse solutions

Abstract

Inverse methods for localization and characterization of cardiac and brain sources from ECG and EEG signals are notoriously ill-conditioned and thus sensitive to SNR in the measurements. Multiple recordings of the same underlying phenomenon are often available, but are contaminated by unmodeled correlated noise such as heart motion from respiration or superposition of atrial activation or on-going EEG in the case of inter-ictal spikes or evoked response in EEG. We address here the open question of how best to incorporate these multiple recordings, comparing standard ensemble averaging, a multichannel non-linear spline-based average designed to be less sensitive to timing variations from motion or modulation, and a probalistic inverse incorporating a data-driven model of the noise correlation and using all recordings jointly. Results are tested on localizations of clincally recorded 120 lead ECGs during ventricular pacing.

Tracking the Position of the Heart From Body Surface Potential Maps and Electrograms

“…However in cases with more densely sampled geometries, inclusion of more organs, or more complex source models, it might be necessary to explore alternative optimization approaches that are computationally less demanding. For example, in addition to analytically-based derivative computation, it may be useful to approximate the geometry with a smaller mesh or interpolate the manifold of forward matrices A ( p b ) with a continuous function that provides simpler analytic gradients and faster computation (Coll-Font, 2016). A second algorithmic consideration is that we are solving a non-linear optimization problem, which can have local minima.…”

“…Finally, an important follow-up to this work will be to incorporate estimation of the heart potentials along with the geometric changes, thus allowing extension of the scope of this method to clinical ECGI settings. Our initial work on this approach indicates that such an extension is possible and may provide useful results (Coll-Font, 2016; Coll-Font et al, 2017); a more extensive evaluation is currently underway.…”

“…In this paper we describe the formulation and experimental validation of our approach. Specifically we reverse the role of geometric assumptions and cardiac surface potentials with respect to the traditional inverse problem of electrocardiography; instead of estimating the electrical sources of the heart from the ECG measurements and the geometry, we correct the geometric model (e.g., translations and rotations of the heart) assuming knowledge of the electrical measurements on both the heart and the body surface (Coll-Font, 2016; Coll-Font et al, 2016b, 2017). Direct application of this approach is relevant to a variety of phantom and animal studies where measurements can be made on both surfaces.…”

A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models

“…For example, it is possible to use the Laplace distribution, which is equivalent to an L1 norm constraint of the solution and used to favor the presence of a small number of large values. Total Variation uses this same distribution —with a covariance matrix equal to the inverse gradient operator— to allow for a small number of large gradients on the heart potentials [11–15]. Other groups have extended this idea to induce sparsity with generalized Gaussian distributions —equivalent to the Lp norm in classical optimization [16,17].…”