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.
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.
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).
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.
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.
Pharmacology, Toxicology and Pharmaceutical Science
1
3%
Biochemistry, Genetics and Molecular Biology
1
3%
Other
3
10%
Unknown
7
23%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 April 2021.
All research outputs
#5,448,088
of 25,377,790 outputs
Outputs from IEEE Transactions on Biomedical Engineering
#695
of 4,673 outputs
Outputs of similar age
#88,297
of 405,873 outputs
Outputs of similar age from IEEE Transactions on Biomedical Engineering
#4
of 25 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,673 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 69% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 405,873 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.
