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).
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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.
Author information Authors and AffiliationsDepartment of Physics, Vanderbilt University, Nashville, TN, USA
Jonathan C. Erickson & L. Alan Bradshaw
Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
Gregory O’Grady, Peng Du, Wenlian Qiao, Andrew J. Pullan & Leo K. Cheng
Department of Surgery, Vanderbilt University School of Medicine, Nashville, TN, USA
Chibuike Obioha, L. Alan Bradshaw & Andrew J. Pullan
University of South Alabama, Mobile, AL, USA
William O. Richards
Department of Physics-Engineering, Washington and Lee University, 204 West Washington Street, Lexington, VA, 24450, USA
Jonathan C. Erickson
Correspondence to Jonathan C. Erickson.
Additional informationAssociate Editor Berj L. Bardakjian oversaw the review of this article.
JCE developed, coded, and analyzed the performance of all automated slow wave detection algorithms. GOG and CO performed the animal surgeries and made serosal electrode recordings. PD, WQ, and LKC developed an initial version of the negative derivative detection signal transform method. AJP, LKC, WOR, and LAB conceived and coordinated the porcine model gastric experiments. JCE and GOG drafted this manuscript.
About this article Cite this articleErickson, J.C., O’Grady, G., Du, P. et al. 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). https://doi.org/10.1007/s10439-009-9870-3
Received: 11 August 2009
Accepted: 07 December 2009
Published: 19 December 2009
Issue Date: April 2010
DOI: https://doi.org/10.1007/s10439-009-9870-3
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