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Fecal incontinence (FI) substantially impairs quality of life and imparts a major socioeconomic burden. Anal sphincter injury and possibly pudendal nerve damage are considered common causes, however, current clinical methods for evaluating their function remain suboptimal. Electromyography (EMG) and pudendal nerve terminal latencies have been applied with some success, but are not considered standard practice due to uncertain accuracy and clinical value. In this study we developed and applied a novel anorectal high-density (HD) EMG probe in humans and pigs to acquire quantitative electrophysiological metrics of the anorectum. In the human trial we assessed somatic pathways and showed that EMG amplitude was greater for tight voluntary squeezes than light voluntary squeezes (0.03 ± 0.02 mV vs. 0.05 ± 0.03 mV). In a porcine model we applied trans-sacral magnetic stimulation to evoke extrinsically activated involuntary pathways and the resulting motor evoked potentials (MEP) were captured using the HD-EMG probe. The mean MEP amplitude at 50% magnetic stimulation intensity output (MSO) was significantly lower that the MEP amplitude at 85, 95 and 100% MSO (1.52 ± 0.50 mV vs. 3.10 ± 0.60 mV). In conclusion, the use of HD-EMG probe in conjunction with trans-sacral magnetic stimulation, for spatiotemporal mapping of anorectal EMG and MEP activity is anticipated to achieve new insights into FI and could offer improved diagnostic and prognostic biomarkers for anorectal dysfunction. [ABSTRACT FROM AUTHOR]

The advent of high-resolution (HR) electrical mapping of slow wave activity has significantly improved the understanding of gastric slow wave activity in normal and dysrhythmic states. One of the current limitations of this technique is it generates a vast amount of data, making manual analysis a tedious task for research and clinical development. In this study we present new automated methods to classify, identify, and locate patterns of interest in gastric slow wave propagation. The classification method uses a similarity metric to classify slow wave propagations, while the identification algorithm uses the divergence and mean curvature of the slow wave propagation to identify and regionalize patterns of interest. The methods were applied to synthetic and experimental datasets and were also compared to manual analysis. The methods classified and identified patterns of slow wave propagation in less than 1 s, compared to manual analysis which took up to 40 min. The automated methods achieved 96% accuracy in classifying AT maps, and 95% accuracy in identifying the propagation pattern with a mean spatial error of 1.5 mm in comparison to manual methods. These new methods will facilitate the efficient translation of gastrointestinal HR mapping techniques to clinical practice. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]