MOTILITY OF THE UNPREPARED INTESTINAL TRACT IN HEALTHY HUMANS AND PATIENTS WITH SLOW TRANSIT CONSTIPATION REVEALED BY USING MICRO-ELECTRONIC CAPSULE
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Published:2021-10-20
Issue:
Volume:
Page:
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ISSN:1016-2372
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Container-title:Biomedical Engineering: Applications, Basis and Communications
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language:en
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Short-container-title:Biomed. Eng. Appl. Basis Commun.
Author:
Cui Liu1,
Huang Jing1,
Shi Yanjiao1,
Yang Hongzhe1
Affiliation:
1. Department of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, P. R. China
Abstract
Slow transit constipation (STC) is usually accompanied by intestinal motility abnormalities. Although conventional anorectal manometry could record the pressure in the colon, most patients need preparation of intestinal tract. The intervention of catheter for monitoring the intestinal pressure also affects the clinical measurement. The pressure data collected by the conventional anorectal manometry cannot fully characterize the dynamic characteristics of the intestine. Thus, we aimed to obtain colonic pressure data under normal physiological conditions. Utilizing these data, we analyze the difference of colonic motility parameters between healthy control and patients with STC. A micro-electronic capsule made by ourselves was used to gather the subjects’ intestinal pressure in their daily life. Several intestinal motility parameters were calculated from the pressure profile. The average energy of colonic pressure data in the STC group is higher than the healthy control group (HC: 259.95 vs. STC: 821.28). But the STC group has a lower average complexity of colonic motility (HC: 0.80 vs. STC: 0.64). About 81.25% of the colonic data from patients with STC could be identified by using slow transit constipation (SVM) classifier. Compared with health control, most colonic parameters of patients with STC are higher under the normal physiological conditions, but the complexity of colonic motility is lower in the STC group. The correct rate of colonic pressure recognition in the STC group is more than 80% by using SVM classifier.
Funder
National Natural Science Foundation of China
Shanghai Alliance Program
Publisher
National Taiwan University
Subject
Biomedical Engineering,Bioengineering,Biophysics
Cited by
1 articles.
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