.png){kind=icon}
返回
Colonic Motility Analysis Using the Wireless Capsule
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2015.2505709.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many disorders are associated with abnormalities in colonic motility; thus, studying colonic motility is important to optimal management. This paper aimed to extract efficient features that characterize colonic motility, observe regional differences of colonic motility, and develop an effective and automated method for classifying colonic motility. Unlike the existing methods for analyzing the colonic motility, 27 features were extracted from 16 healthy subjects and 32 patients with constipation based on wavelet coefficients. Thereafter, the Wilcoxon rank sum test was used to select features, and finally, the adaboost-backpropagation neural network was used to classify the colonic motility. To evaluate the performance of the proposed method, the results were compared with the support vector machine and the back propagation network according to sensitivity, specificity, and accuracy. High prediction accuracy, sensitivity, and specificity confirm the effectiveness of the proposed methodology.
Keyword:
Feature extraction
back propagation network
support vector machine
colonic motility classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
分享收藏Application of Multi-rotor Unmanned Aerial Vehicle Application in Management of Stem Borer (Lepidoptera) in Sugarcane.png)
Sugar Tech
Sugar TechIF0
PREAI
分享收藏An Implantable Telemetry Platform System With ASIC for in Vivo Monitoring of Gastrointestinal Physiological InformationIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏
分享收藏How to Assess Regional and Whole Gut Transit Time With Wireless Motility Capsule如何使用无线运动胶囊评估区域和整个肠道通过时间
OAAI
OAAI分享收藏
查看更多内容