Heart Position Uncertainty Quantification in the Inverse Problem of ECGI

Jake Bergquist1, Lindsay Rupp1, Anna Busatto1, Ben Orkild1, Brian Zenger1, Wilson Good2, Jaume Coll-Font3, Jess Tate1, Dana Brooks4, Akil Narayan1, Rob MacLeod1
1University of Utah, 2The SCI Institute, 3Massachusetts General Hospital, 4Northeastern University


Electrocardigraphic imaging (ECGI) is a clinical and research tool for noninvasive diagnosis of cardiac electrical dysfunction. The position of the heart within the torso is both an input and a common source of error in ECGI. As ECGI becomes a more widely available research and clinical tool, it is critical to understand how uncertainty in the heart position can affect ECGI solutions. Many studies have sought to improve cardiac localization accuracy in the context of ECGI, however, few have examined the effects of heart position uncertainty on ECGI results. None to our knowledge have sought to account for the heart position uncertainty in the ECGI solution. Uncertainty quantification (UQ) is the study of how variability in model inputs, for example, heart position, affects the model output. Recently developed UQ tools enable the robust application of UQ to ECGI reconstructions. In this study, we develop an ECGI formulation that for the first time directly incorporates uncertainty in the heart position into the ECGI solution. The result is an ECGI solution that is robust to variation in heart position. Using data from two Langendorff experimental preparations, each with 120 heartbeats spread over three different activation sequences, we found that as heart position uncertainty increased above ±10 mm the ECGI solution quality degraded. However, even at large heart position uncertainty (±40 mm) our novel UQ ECGI formulation produced reasonable ECGI solutions (root mean squared error < 1 mV, spatial correlation >0.6, temporal correlation >0.75). Our UQ ECGI formulation allows for examination of the performance and robustness of ECGI under uncertainty in the input parameters