Falling-Edge, Variable Threshold (FEVT) Method for the Automated Detection of Gastric Slow Wave Events in High-Resolution Serosal Electrode Recordings
- 240 Downloads
High resolution (HR) multi-electrode mapping is increasingly being used to evaluate gastrointestinal slow wave behaviors. To create the HR activation time (AT) maps from gastric serosal electrode recordings that quantify slow wave propagation, it is first necessary to identify the AT of each individual slow wave event. Identifying these ATs has been a time consuming task, because there has previously been no reliable automated detection method. We have developed an automated AT detection method termed falling-edge, variable threshold (FEVT) detection. It computes a detection signal transform to accentuate the high ‘energy’ content of the falling edges in the serosal recording, and uses a running median estimator of the noise to set the time-varying detection threshold. The FEVT method was optimized, validated, and compared to other potential algorithms using in vivo HR recordings from a porcine model. FEVT properly detects ATs in a wide range of waveforms, making its performance substantially superior to the other methods, especially for low signal-to-noise ratio (SNR) recordings. The algorithm offered a substantial time savings (>100 times) over manual-marking whilst achieving a highly satisfactory sensitivity (0.92) and positive-prediction value (0.89).
KeywordsGastric electrical activity Gastric slow wave Activation time map High-resolution mapping Energy operator
The authors gratefully acknowledge the assistance of the animal care facilities at their respective Universities, including Linley Nisbett for her technical skills. This work was supported by NIH Grants R01 DK58197, RO1 DK58697-02, RO1 DK64775, and grants from the NZ Society of Gastroenterology and NZ Health Research Council.
- 3.Cheng, L. K., G. O’Grady, P. Du, J. U. Egbuji, J. A. Windsor, and A. J. Pullan. Gastrointestinal system. Wiley Interdiscip. Rev.: Syst. Biol. Med., 2009. doi: 10.1002/wsbm.19.
- 5.Du, P., G. O’Grady, J. U. Egbuji, W. J. Lammers, D. Budgett, P. Nielsen, J. A. W. A. J. Pullan, and L. K. Cheng. High resolution mapping of in-vivo gastrointestinal slow wave activity using flexible printed circuit board electrodes: methods and validation. Ann. Biomed. Eng. 37:839–846, 2009.CrossRefPubMedGoogle Scholar
- 7.Du, P., W. Qiao, G. O’Grady, J. U. Egbuji, W. 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. Soc. 1:2527–2530, 2009.Google Scholar
- 11.Kaiser, J. On a simple algorithm to calculate the energy of a signal. In: Proceedings of the IEEE International Conference on Acoustic Speech and Signal Processing, edited by Y. Smith, Albuquerque, April 1990, pp. 381–384.Google Scholar
- 12.Kaiser, J. Some useful properties of Teager’s energy operators. In: 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 3. ICASSP-93, 1993.Google Scholar
- 14.Kohler, B., C. Hennig, and R. Orglmeister. The principles of software QRS detection. In: IEEE Engineering in Medicine and Biology, Jan/Feb 2002, pp. 42–57.Google Scholar
- 15.Lammers, W. Smoothmap v3.0.3. http://www.Smoothmap.org.
- 22.McAdams, E. Bioelectrodes. In: Encyclopedia of Medical Devices and Instrumentation (2nd ed.), edited by J. G. Webster. New York: Wiley, 2006, pp. 120–166.Google Scholar
- 25.O’Grady, G., P. Du, J. Egbuji, W. Lammers, D. Budgett, P. Nielsen, J. Windsor, A. Pullan, and L. Cheng. High-resolution mapping of human gastric slow wave activity: methods and first results. Gastroenterology 136:A484, 2009.Google Scholar
- 31.Shenasa, M., G. Hindricks, M. Borggrefe, and G. Breithardt. Cardiac Mapping. Oxford, UK: Blackwell Publishing Ltd, 2009.Google Scholar