Jonathan C Erickson1, Joy Putney2, Douglas Hilbert3, Niranchan Paskaranandavadivel4, Leo K Cheng5, Greg O'Grady5, Timothy R Angeli4. 1. Department of Physics and Engineering, Washington and Lee University, Lexington, VA, USA. 2. Department of Physics and Engineering, Washington and Lee University. 3. Departments of Mathematics and Biochemistry, Washington and Lee University. 4. Auckland Bioengineering InstituteUniversity of Auckland. 5. Auckland Bioengineering Institute.
Abstract
OBJECTIVE: The aim of this study was to develop, validate, and apply a fully automated method for reducing large temporally synchronous artifacts present in electrical recordings made from the gastrointestinal (GI) serosa, which are problematic for properly assessing slow wave dynamics. Such artifacts routinely arise in experimental and clinical settings from motion, switching behavior of medical instruments, or electrode array manipulation. METHODS: A novel iterative Covariance-Based Reduction of Artifacts (COBRA) algorithm sequentially reduced artifact waveforms using an updating across-channel median as a noise template, scaled and subtracted from each channel based on their covariance. RESULTS: Application of COBRA substantially increased the signal-to-artifact ratio (12.8 ± 2.5 dB), while minimally attenuating the energy of the underlying source signal by 7.9% on average ( -11.1 ± 3.9 dB). CONCLUSION: COBRA was shown to be highly effective for aiding recovery and accurate marking of slow wave events (sensitivity = 0.90 ± 0.04; positive-predictive value = 0.74 ± 0.08) from large segments of in vivo porcine GI electrical mapping data that would otherwise be lost due to a broad range of contaminating artifact waveforms. SIGNIFICANCE: Strongly reducing artifacts with COBRA ultimately allowed for rapid production of accurate isochronal activation maps detailing the dynamics of slow wave propagation in the porcine intestine. Such mapping studies can help characterize differences between normal and dysrhythmic events, which have been associated with GI abnormalities, such as intestinal ischemia and gastroparesis. The COBRA method may be generally applicable for removing temporally synchronous artifacts in other biosignal processing domains.
OBJECTIVE: The aim of this study was to develop, validate, and apply a fully automated method for reducing large temporally synchronous artifacts present in electrical recordings made from the gastrointestinal (GI) serosa, which are problematic for properly assessing slow wave dynamics. Such artifacts routinely arise in experimental and clinical settings from motion, switching behavior of medical instruments, or electrode array manipulation. METHODS: A novel iterative Covariance-Based Reduction of Artifacts (COBRA) algorithm sequentially reduced artifact waveforms using an updating across-channel median as a noise template, scaled and subtracted from each channel based on their covariance. RESULTS: Application of COBRA substantially increased the signal-to-artifact ratio (12.8 ± 2.5 dB), while minimally attenuating the energy of the underlying source signal by 7.9% on average ( -11.1 ± 3.9 dB). CONCLUSION: COBRA was shown to be highly effective for aiding recovery and accurate marking of slow wave events (sensitivity = 0.90 ± 0.04; positive-predictive value = 0.74 ± 0.08) from large segments of in vivo porcine GI electrical mapping data that would otherwise be lost due to a broad range of contaminating artifact waveforms. SIGNIFICANCE: Strongly reducing artifacts with COBRA ultimately allowed for rapid production of accurate isochronal activation maps detailing the dynamics of slow wave propagation in the porcine intestine. Such mapping studies can help characterize differences between normal and dysrhythmic events, which have been associated with GI abnormalities, such as intestinal ischemia and gastroparesis. The COBRA method may be generally applicable for removing temporally synchronous artifacts in other biosignal processing domains.
Authors: Timothy R Angeli; Peng Du; Niranchan Paskaranandavadivel; Patrick W M Janssen; Arthur Beyder; Roger G Lentle; Ian P Bissett; Leo K Cheng; Gregory O'Grady Journal: J Physiol Date: 2013-05-27 Impact factor: 5.182
Authors: Timothy R Angeli; Gregory O'Grady; Niranchan Paskaranandavadivel; Jonathan C Erickson; Peng Du; Andrew J Pullan; Ian P Bissett; Leo K Cheng Journal: J Neurogastroenterol Motil Date: 2013-04-16 Impact factor: 4.924
Authors: T R Angeli; P Du; N Paskaranandavadivel; S Sathar; A Hall; S J Asirvatham; G Farrugia; J A Windsor; L K Cheng; G O'Grady Journal: Neurogastroenterol Motil Date: 2016-12-29 Impact factor: 3.598