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

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.