Reconstruction of cardiac position using body surface potentials. (March 2022)

• Record Type: Journal Article
• Authors: Bergquist, Jake A.; Coll-Font, Jaume; Zenger, Brian; Rupp, Lindsay C.; Good, Wilson W.; Brooks, Dana H.; MacLeod, Rob S.
• Is Part Of: Computers in biology and medicine. Volume 142(2022)
• Publication Date: 2022-03
• Subjects: ECGI Electrocardiographic Imaging; Electrocardiographic imaging -- Body surface potential mapping -- Inverse problem -- Optimization; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105174
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Electrocardiographic imaging (ECGI) is a noninvasive technique to assess the bioelectric activity of the heart which has been applied to aid in clinical diagnosis and management of cardiac dysfunction. ECGI is built on mathematical models that take into account several patient specific factors including the position of the heart within the torso. Errors in the localization of the heart within the torso, as might arise due to natural changes in heart position from respiration or changes in body position, contribute to errors in ECGI reconstructions of the cardiac activity, thereby reducing the clinical utility of ECGI. In this study we present a novel method for the reconstruction of cardiac geometry utilizing noninvasively acquired body surface potential measurements. Our geometric correction method simultaneously estimates the cardiac position over a series of heartbeats by leveraging an iterative approach which alternates between estimating the cardiac bioelectric source across all heartbeats and then estimating cardiac positions for each heartbeat. We demonstrate that our geometric correction method is able to reduce geometric error and improve ECGI accuracy in a wide range of testing scenarios. We examine the performance of our geometric correction method using different activation sequences, ranges of cardiac motion, and body surface electrode configurations. We find that after geometric correction resulting ECGI solution accuracy is improved and variability of the ECGI solutions between heartbeats is substantially reduced. Highlights: Noninvasively recorded body surface potentials can be used to estimate cardiac position. ECGI showed improved accuracy and reduced solution variability when using optimized cardiac positions. Substantial cardiac localization accuracy was achieved with as few as 16 body surface electrodes.