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.
Highlights
Body surface potential mapping (BSPM) has a long and rich history as a noninvasive technique used to sample the heart’s electrical activity by sampling over the entire surface of the thorax
We provide a contemporary view of BSPM and its value in exploring mechanisms of electrocardiography as well as its clinical potential in the setting of electrocardiographic imaging and emerging applications of machine learning
Wave, or signal shapes, e.g., P, QRS, or T-wave shapes, durations, and symmetry. Most such features are represented in any type of ECG and their use in BSPM assumes that enhanced spatial sampling will yield improved diagnostics, a hypothesis supported by many clinical applications [4,6,9,10,11,12,13]
Summary
Body surface potential mapping (BSPM) has a long and rich history as a noninvasive technique used to sample the heart’s electrical activity by sampling over the entire surface of the thorax. BSPM was first reported widely by Taccardi et al as a tool to demonstrate the inadequacies of single-dipole source models to describe cardiac electric sources [8] and many other investigators have demonstrated its superior ability to reveal a wide range of pathologies [6]. It remains a useful tool for both research studies and as the input for an imaging modality that seeks to reconstruct cardiac electrical activity noninvasively. We provide a contemporary view of BSPM and its value in exploring mechanisms of electrocardiography as well as its clinical potential in the setting of electrocardiographic imaging and emerging applications of machine learning
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