Literature DB >> 35665072

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives.

Jake Bergquist1,2,3, Lindsay Rupp1,2,3, Brian Zenger1,2,3,4, James Brundage4, Anna Busatto1,2, Rob S MacLeod1,2,3.   

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.

Entities:  

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

Year:  2021        PMID: 35665072      PMCID: PMC9164986          DOI: 10.3390/hearts2040040

Source DB:  PubMed          Journal:  Hearts (Basel)        ISSN: 2673-3846


  146 in total

1.  Imaging dispersion of myocardial repolarization, II: noninvasive reconstruction of epicardial measures.

Authors:  R N Ghanem; J E Burnes; A L Waldo; Y Rudy
Journal:  Circulation       Date:  2001-09-11       Impact factor: 29.690

2.  The electrophysiological cardiac ventricular substrate in patients after myocardial infarction: noninvasive characterization with electrocardiographic imaging.

Authors:  Phillip S Cuculich; Junjie Zhang; Yong Wang; Kavit A Desouza; Ramya Vijayakumar; Pamela K Woodard; Yoram Rudy
Journal:  J Am Coll Cardiol       Date:  2011-10-25       Impact factor: 24.094

3.  Lead system transformation of body surface map data.

Authors:  R Hoekema; G J Uijen; D Stilli; A van Oosterom
Journal:  J Electrocardiol       Date:  1998-04       Impact factor: 1.438

4.  Cardiac position sensitivity study in the electrocardiographic forward problem using stochastic collocation and boundary element methods.

Authors:  Darrell J Swenson; Sarah E Geneser; Jeroen G Stinstra; Robert M Kirby; Rob S MacLeod
Journal:  Ann Biomed Eng       Date:  2011-09-10       Impact factor: 3.934

5.  Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping.

Authors:  Sophie Giffard-Roisin; Thomas Jackson; Lauren Fovargue; Jack Lee; Herve Delingette; Reza Razavi; Nicholas Ayache; Maxime Sermesant
Journal:  IEEE Trans Biomed Eng       Date:  2016-11-16       Impact factor: 4.538

6.  Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning.

Authors:  James N Brundage; Vai Suliafu; Jake A Bergquist; Brian Zenger; Lindsay C Rupp; Bao Wang; Rob MacLeod
Journal:  Comput Cardiol (2010)       Date:  2021-09

7.  Assessment of regularization techniques for electrocardiographic imaging.

Authors:  Matija Milanič; Vojko Jazbinšek; Robert S Macleod; Dana H Brooks; Rok Hren
Journal:  J Electrocardiol       Date:  2013-10-17       Impact factor: 1.438

8.  Noninvasive panoramic mapping of human atrial fibrillation mechanisms: a feasibility report.

Authors:  Michel Haissaguerre; Meleze Hocini; Ashok J Shah; Nicolas Derval; Frederic Sacher; Pierre Jais; Remi Dubois
Journal:  J Cardiovasc Electrophysiol       Date:  2013-02-01

9.  Vulnerability to ventricular arrhythmia: assessment by mapping of body surface potential.

Authors:  M J Gardner; T J Montague; C S Armstrong; B M Horacek; E R Smith
Journal:  Circulation       Date:  1986-04       Impact factor: 29.690

10.  PFEIFER: Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings.

Authors:  Anton Rodenhauser; Wilson W Good; Brian Zenger; Jess Tate; Kedar Aras; Brett Burton; Rob S MacLeod
Journal:  J Open Source Softw       Date:  2018
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