William L. Maner

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OBJECTIVE To use artificial neural networks (ANNs) on uterine electromyography (EMG) data to classify term/preterm labor/non-labor pregnant patients. MATERIALS AND METHODS A total of 134 term and 51 preterm women (all ultimately delivered spontaneously) were included. Uterine EMG was measured trans-abdominally using surface electrodes. "Bursts" of(More)
Diabetic gastroparesis is a disorder that predominantly affects women. However, the biological basis of this sex bias remains completely unknown. In this study we tested the hypothesis that a component of this effect may be mediated by the nitrergic inhibitory system of the enteric nervous system. Age-matched male and female Sprague-Dawley rats were studied(More)
The present work seeks to determine if a particular non-linear analytic method is effective at quantifying uterine electromyography (EMG) data for estimating the onset of labor. Twenty-seven patients were included, and their uterine EMG was recorded non-invasively for 30 min. The patients were grouped into two sets: G1: labor, N = 14; G2: antepartum, N =(More)
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