Falling-edge, variable threshold (FEVT) method for the automated detection of gastric slow wave events in high-resolution serosal electrode recordings.

Erickson JC, O'Grady G, Du P, Obioha C, Qiao W, Richards WO, Bradshaw LA, Pullan AJ, Cheng LK
Ann Biomed Eng. 2010 38 (4): 1511-29

PMID: 20024624 · PMCID: PMC2855965 · DOI:10.1007/s10439-009-9870-3

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).

MeSH Terms (10)

Algorithms Animals Electrodes, Implanted Electromyography Muscle, Smooth Myoelectric Complex, Migrating Pattern Recognition, Automated Signal Processing, Computer-Assisted Stomach Swine

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