Automated Gastric Slow Wave Cycle Partitioning and Visualization for High-resolution Activation Time Maps
- 174 Downloads
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.
KeywordsGastric electrical activity High-resolution mapping Activation map Automated detection Cycle partitioning
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.
- 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
- 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.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.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
- 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
- 10.Josephson, M. Clinical cardiac electrophysiology: techniques and interpretations. Adis, 2002.Google Scholar
- 11.Lammers, W. Smoothmap [computer program]. version 3.05., http://www.smoothmap.org, March 2009.
- 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
- 24.Shenasa, M., G. Hindricks, M. Borggrefe, and G. Breithardt. Cardiac Mapping, 3rd edn. Oxford, UK: Blackwell Publishing Ltd, 2009.Google Scholar