Advertisement

Annals of Biomedical Engineering

, Volume 39, Issue 1, pp 469–483 | Cite as

Automated Gastric Slow Wave Cycle Partitioning and Visualization for High-resolution Activation Time Maps

  • Jonathan C. EricksonEmail author
  • Greg O’Grady
  • Peng Du
  • John U. Egbuji
  • Andrew J. Pullan
  • Leo K. Cheng
Article

Abstract

High-resolution (HR) multi-electrode mapping has become an important technique for evaluating gastrointestinal (GI) slow wave (SW) behaviors. However, the application and uptake of HR mapping has been constrained by the complex and laborious task of analyzing the large volumes of retrieved data. Recently, a rapid and reliable method for automatically identifying activation times (ATs) of SWs was presented, offering substantial efficiency gains. To extend the automated data-processing pipeline, novel automated methods are needed for partitioning identified ATs into their propagation cycles, and for visualizing the HR spatiotemporal maps. A novel cycle partitioning algorithm (termed REGROUPS) is presented. REGROUPS employs an iterative REgion GROwing procedure and incorporates a Polynomial-surface-estimate Stabilization step, after initiation by an automated seed selection process. Automated activation map visualization was achieved via an isochronal contour mapping algorithm, augmented by a heuristic 2-step scheme. All automated methods were collectively validated in a series of experimental test cases of normal and abnormal SW propagation, including instances of patchy data quality. The automated pipeline performance was highly comparable to manual analysis, and outperformed a previously proposed partitioning approach. These methods will substantially improve the efficiency of GI HR mapping research.

Keywords

Gastric electrical activity High-resolution mapping Activation map Automated detection Cycle partitioning 

Notes

Acknowledgments

This study and/or authors are partially funded by grants from the New Zealand HRC, the NIH (RO1 DK64775), and the American Neurogastroenterology and Motility Society (ANMS), and the NZ Society of Gastroenterology/Ferring Pharmaceuticals Research Fellowship. The authors gratefully acknowledge the technical assistance of Linley Nisbet at the University of Auckland animal care facility. The authors also thank anonymous reviewers for their helpful comments.

Supplementary material

10439_2010_170_MOESM1_ESM.pdf (61 kb)
Supplementary Figure 1 (PDF 61kb)

References

  1. 1.
    Bayly, P., B. KenKnight, J. Rogers, R. Hillsley, I. Raymond, and W. Smith. Estimation of conduction velocity vector fields from epicardial mapping data. IEEE Trans. Biomed. Eng. 45:563–571, 1998.CrossRefPubMedGoogle Scholar
  2. 2.
    Chen, J., B. Schirmer, and R. McCallum. Serosal and cutaneous recordings of gastric myoelectrical activity in patients with gastroparesis. Am. J. Physiol. Gastrointest. Liver Physiol. 266:G90, 1994.Google Scholar
  3. 3.
    Cucchiara, S., G. Salvia, O. Borrelli, E. Ciccimarra, N. Az-Zeqeh, S. Rapagiolo, R. Minella, A. Campanozzi, and G. Riezzo. Gastric electrical dysrhythmias and delayed gastric emptying in gastroesophageal reflux disease. Am. J. Gastroenterol. 92:1103–1108, 1997.PubMedGoogle Scholar
  4. 4.
    Du, P., G. O’Grady, L. K. Cheng, and A. J. Pullan. A multi-scale model of the electrophysiological basis of the electrogastrogram. Biophys. J. (in press).Google Scholar
  5. 5.
    Du, P., G. O’Grady, J. U. Egbuji, W. J. Lammers, D. Budgett, P. Nielsen, J. A. Windsor, A. J. Pullan, and L. K. Cheng. High-resolution mapping of in vivo gastrointestinal slow wave activity using flexible printed circuit board electrodes: methodology and validation. Ann. Biomed. Eng. 37:839–46, 2009.CrossRefPubMedGoogle Scholar
  6. 6.
    Du, P., W. Qiao, G. O’Grady, J. Egbuji, W. J. Lammers, L. K. Cheng, and A. J. Pullan. Automated detection of gastric slow-wave events and estimation of propagation velocity vector fields from serosal high-resolution mapping. Conf. Proc. IEEE Eng. Med. Biol. Sci. 2009:2527–2530, 2009.Google Scholar
  7. 7.
    Egbuji, J., G. O’Grady, P. Du, W. Lammers, L. Cheng, J. Windsor, and A. Pullan. Origin, propagation and regional characteristics of porcine gastric slow wave activity defined by high-resolution electrical mapping. Neurogastroenterol. Motil. 22:e292–e300, 2010.CrossRefPubMedGoogle Scholar
  8. 8.
    Erickson, J., G. O’Grady, P. Du, C. Obioha, W. Qiao, W. O. Richards, L. A. Bradshaw, A. J. Pullan, and L. K. Cheng. Falling-edge, variable threshold (FEVT) method for the automated detection of gastric slow wave events in high-resolution serosal electrode recordings. Ann. Biomed. Eng. 38:1511–1529, 2010.CrossRefPubMedGoogle Scholar
  9. 9.
    Farrugia, G. Interstitial cells of cajal in health and disease. Neurogastroenterol. Motil. 20 Suppl 1:54–63, 2008.CrossRefPubMedGoogle Scholar
  10. 10.
    Josephson, M. Clinical cardiac electrophysiology: techniques and interpretations. Adis, 2002.Google Scholar
  11. 11.
    Lammers, W. Smoothmap [computer program]. version 3.05., http://www.smoothmap.org, March 2009.
  12. 12.
    Lammers, W. J., A. el Kays, K. Arafat, and T. el Sharkawy. Wave mapping: detection of co-existing multiple wavefronts in high-resolution electrical mapping. Med. Biol. Eng. Comput. 33:476–481, 1995.CrossRefPubMedGoogle Scholar
  13. 13.
    Lammers, W. J., B. Michiels, J. Veoten, L. Ver Donck, and J. A. Schuurkes. Mapping slow waves and spikes in chronically instrumented conscious dogs: automated on-line electrogram analysis. Med. Biol. Eng. Comput. 46:121–129, 2008.CrossRefPubMedGoogle Scholar
  14. 14.
    Lammers, W. J., L. Ver Donck, B. Stephen, D. Smets, and J. A. Schuurkes. Focal activities and re-entrant propagations as mechanisms of gastric tachyarrhythmias. Gastroenterology 135:1601–11, 2008.CrossRefPubMedGoogle Scholar
  15. 15.
    Lammers, W. J., L. Ver Donck, B. Stephen, D. Smets, and J. A. Schuurkes. Origin and propagation of the slow wave in the canine stomach: the outlines of a gastric conduction system. Am. J. Physiol. Gastrointest. Liver Physiol. 296:G1200–G1210, 2009.CrossRefPubMedGoogle Scholar
  16. 16.
    Lin, X., and J. Chen. Abnormal gastric slow waves in patients with functional dyspepsia assessed by multichannel electrogastrography. Am. J. Physiol. Gastrointest. Liver Physiol. 280:G1370–G1375, 2001.PubMedGoogle Scholar
  17. 17.
    O’Grady, G., P. Du, L. K. Cheng, J. Egbuji, W. Lammers, J. A. Windsor, and A. Pullan. The origin and propagation of human gastric slow wave activity defined by high-resolution mapping. Am. J. Physiol. Gastrointest. Liver Physiol. 299:585–592, 2010.CrossRefGoogle Scholar
  18. 18.
    O’Grady, G., P. Du, W. J. Lammers, J. U. Egbuji, P. Mithraratne, J. D. Chen, L. K. Cheng, J. A. Windsor, and A. J. Pullan. High-resolution entrainment mapping of gastric pacing: a new analytic tool. Am. J. Physiol. Gastrointest. Liver Physiol. 298:314–321, 2010.CrossRefGoogle Scholar
  19. 19.
    Ordog, T. Interstitial cells of Cajal in diabetic gastroenteropathy. Neurogastroenterol. Motil. 20:8–18, 2008.CrossRefPubMedGoogle Scholar
  20. 20.
    Rogers, J., and P. Bayly. Quantitative analysis of complex rhythms. In: Quantitative Cardiac Electrophysiology, 3rd ed., edited by C. Cabo and D. S. Rosenbaum. New York: Marcel Decker Inc., 2002, pp. 403–428.Google Scholar
  21. 21.
    Rogers, J., P. Bayly, R. Ideker, and W. Smith. Quantitative techniques for analyzing high-resolution cardiac-mapping data. IEEE Eng. Med. Biol. Mag. 17:62–72, 1998.CrossRefPubMedGoogle Scholar
  22. 22.
    Rogers, J., M. Usui, B. KenKnight, R. Ideker, and W. Smith. A quantitative framework for analyzing epicardial activation patterns during ventricular fibrillation. Ann. Biomed. Eng. 25:749–760, 1997.CrossRefPubMedGoogle Scholar
  23. 23.
    Sanders, K. M., S. D. Koh, and S. M. Ward. Interstitial cells of Cajal as pacemakers in the gastrointestinal tract. Ann. Rev. Physiol. 68:307–43, 2006.CrossRefGoogle Scholar
  24. 24.
    Shenasa, M., G. Hindricks, M. Borggrefe, and G. Breithardt. Cardiac Mapping, 3rd edn. Oxford, UK: Blackwell Publishing Ltd, 2009.Google Scholar
  25. 25.
    Winfree, A. Heart muscle as a reaction-diffusion medium: The roles of electric potential diffusion, activation front curvature, and anisotropy. Int. J. Bifurcat. Chaos 7:487–526, 1997.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2010

Authors and Affiliations

  • Jonathan C. Erickson
    • 1
    Email author
  • Greg O’Grady
    • 2
    • 3
  • Peng Du
    • 2
  • John U. Egbuji
    • 2
    • 3
  • Andrew J. Pullan
    • 2
    • 4
    • 5
  • Leo K. Cheng
    • 2
  1. 1.Department of Physics-EngineeringWashington and Lee UniversityLexingtonUSA
  2. 2.Auckland Bioengineering InstituteThe University of AucklandAucklandNew Zealand
  3. 3.Department of SurgeryThe University of AucklandAucklandNew Zealand
  4. 4.Department of Engineering ScienceThe University of AucklandAucklandNew Zealand
  5. 5.Department of SurgeryVanderbilt UniversityNashvilleUSA

Personalised recommendations