Evidence map›Paper›PMID 27071158›Full record

ArticleIEEE transactions on bio-medical engineering2017

Time-Delay Mapping of High-Resolution Gastric Slow-Wave Activity.

Niranchan Paskaranandavadivel, Gregory OGrady, Leo K Cheng

Abstract read
In one paragraph

Article in IEEE transactions on bio-medical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Niranchan Paskaranandavadivel
Gregory OGrady
Leo K Cheng

Funding

Realistic models of gastrointestinal bioelectromagnetismR01DK064775 · NIDDK · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CHENG, LEO · 2004 to 2014
$2.6M
NIDDK NIH HHS R01 DK064775
6 · The paper itself

Abstract

goalAnalytic monitoring of electrophysiological data has become an essential component of efficient and accurate clinical care. In the gastrointestinal (GI) field, recent advances in high-resolution (HR) mapping are now providing critical information about spatiotemporal profiles of slow-wave activity in normal and disease (dysrhythmic) states. The current approach to analyze GI HR electrophysiology data involves the identification of individual slow-wave events in the electrode array, followed by tracking and clustering of events to create a spatiotemporal map. This method is labor and computationally intensive and is not well suited for real-time clinical use or chronic monitoring.

methodsIn this study, an automated novel technique to assess propagation patterns was developed. The method utilized time delays of the slow-wave signals which was computed through cross correlations to calculate velocity. Validation was performed with both synthetic and human and porcine experimental data.

resultsThe slow-wave profiles computed via the time-delay method compared closely with those computed using the traditional method (speed difference: 7.2% ± 2.6%; amplitude difference: 8.6% ± 3.5%, and negligible angle difference).

conclusionThis novel method provides rapid and intuitive analysis and visualization of slow-wave activity. SIGNIFICANCE: This techniques will find major applications in the clinical translation of acute and chronic HR electrical mapping for motility disorders, and act as a screening tool for detailed detection and tracking of individual propagating wavefronts, without the need for comprehensive standard event-detection analysis.

Indexed as

Action PotentialsAlgorithmsAnimalsElectromyographyGastric EmptyingHumansMuscle, SmoothMyoelectric Complex, MigratingNeural ConductionReproducibility of ResultsSensitivity and SpecificityStomachSwine

Identifiers

PMID27071158
PMCPMC5292208

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.