1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

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

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <div class="section"> <a class="named-anchor" id="S1"> <!-- named anchor --> </a> <h5 class="section-title" id="d723322e113">Goal</h5> <p id="P1">Analytic 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 analyzing 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. </p> </div><div class="section"> <a class="named-anchor" id="S2"> <!-- named anchor --> </a> <h5 class="section-title" id="d723322e118">Methods</h5> <p id="P2">In 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. </p> </div><div class="section"> <a class="named-anchor" id="S3"> <!-- named anchor --> </a> <h5 class="section-title" id="d723322e123">Results</h5> <p id="P3">The 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). </p> </div><div class="section"> <a class="named-anchor" id="S4"> <!-- named anchor --> </a> <h5 class="section-title" id="d723322e128">Conclusion</h5> <p id="P4">This novel method provides rapid and intuitive analysis and visualization of slow wave activity. </p> </div><div class="section"> <a class="named-anchor" id="S5"> <!-- named anchor --> </a> <h5 class="section-title" id="d723322e133">Significance</h5> <p id="P5">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. </p> </div>

          Related collections

          Author and article information

          Journal
          IEEE Transactions on Biomedical Engineering
          IEEE Trans. Biomed. Eng.
          Institute of Electrical and Electronics Engineers (IEEE)
          0018-9294
          1558-2531
          January 2017
          January 2017
          : 64
          : 1
          : 166-172
          Article
          10.1109/TBME.2016.2548940
          5292208
          27071158
          9e11a4fb-dabd-4318-a89f-62f1502df595
          © 2017
          History

          Comments

          Comment on this article