Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

Abstract

Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.

Keywords: body surface mapping; clinical applications; electrocardiographic imaging; image processing.

Figure 1.

BSPM analysis approaches. BSPM signals are analyzed using one of three different pathways and using two types of mathematical models. Signal analysis methods generally operate on the BSP signals isolated from their geometry. Map analysis extends signal analysis by including the geometry of the torso from which the BSP are recorded. Both signal analysis and map analysis usually rely predominately on statistical models. ECG imaging is based predominately on a deterministic model to reconstructing the cardiac activity at the heart (see the cutaway in the last panel) using the BSP signals and the geometry of the thorax.

Figure 2.

Body surface mapping lead arrangements and torso geometry examples. Bordeaux Torso Tank array (A) [88]. Utah Torso Tank array (B) [89]. Utah Large Animal Body Surface Map (C) [90]. Maastricht Dog Torso Map (D) [58]. EP Solutions patient 24 (E) [91]. KIT 20 PVC torso (F) Karlsruhe Institute of Technology. Nijmegen Human Torso 2004–12-09 (G) University of Nijmegen. Dalhausi Human Torso (H) ([82]). These geometries and associated body surface data (except C) can be found on EDGAR, a cardiac electrophysiology open database (edgar.sci.utah.edu) (accessed date 29 October 2021) [92].

Figure 3.

An example BSP map with timepoint of interest visualized. The signal shown is from a stimulated ventricular activation from the anterior left ventricle. The time singnal (bottom) is the RMS of the torso surface signals. The time instances shown are the peak of the RMS QRS, the end of the QRS, and the peak of the T-wave.

Figure 4.

Custom signal acquisition system described by Zenger et al. [90]. This system includes custom electrode arrays, a front-end interface for connecting various electrode array configurations, analog processing, analog to digital conversion by a commercial intan recording system, and data visualization and saving software. The ADC and display software are designed by Intan Technologies (intantech.com) (accessed date 29 October 2021)

Figure 5.

Example ECGI implementation. Recorded body surface potentials (left) are combined with a geometric model and a source model. The geometric model is made up of the relative positions of the cardiac geometry and torso geometry. The source model in this case is extracelular potentials, and the relationship used for the forward model is the boundary element method. The resulting inverse estimation is extracellular potentials on the cardiac surface. The final column shows a comparison between the inverse solution and the measured extracellular potentials on a flattened version of the cardiac geometry. The cardiac and torso geometries were generated as described in Bergquist et al. [113] where the cardiac geometry is a 256 electrode pericardiac cage array and the torso geometry is a 192 electrode torso tank. Tikhonov 2nd order regularization with L curve was used. The peak of the RMS of the QRS was visualized in all steps.

Figure 6.

Transformation of BSP maps into inputs for various types of machine learning. BSPM signals are first preprocessed, which varies depending on the type of ML model. For feature-based models, characteristics of the BSP signals (QRS integral, T wave peak, activation time, etc.) are calculated and provided as the input signals. For simple linear neural networks and other vector-based ML models the BSP signals are linearized, concatenating the signal form each electrodes into a single vector. For image- and natural language-based ML models, the BSP signals are arranged into a matrix of m leads by n electrodes, which can then be spilt into s length words. For graph-based ML models, the torso geometry is used to create a computational graph that relates the BSP signals to each other based on their spatial relationships.