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计算机辅助分析人体小肠连续动态测压记录

Computer-supported analysis of continuous ambulatory manometric recordings in the human small bowel.

作者信息

Andrioli A, Wilmer A, Coremans G, Vandewalle J, Janssens J

机构信息

Center for Gastrointestinal Research, University of Leuven, Belgium.

出版信息

Med Biol Eng Comput. 1996 Sep;34(5):336-43. doi: 10.1007/BF02520000.

Abstract

An algorithm has been developed for the offline analysis of prolonged manometric recordings in the upper small intestine of humans. Sample data are acquired in the human duodenum and jejunum six solid-state strain-gauge transducers mounted on a silicon catheter that is connected to a portable digital recording device. The data are sampled at 4 Hz and filtered. For accurate calculations, the filtered signals are converted to cubic B-spline functions of order four. Based on an exponential weighted moving average, a base-line is calculated from the signal. Contractions are recognised on the basis of thresholds for minimum amplitude and duration. The developed algorithm calculates properties of these contractions, such as amplitude, duration, area and a motility index. In addition, the program automatically recognises normal motor patterns of the fasted human small intestine, such as the migrating motor complex, and aids in the identification of the postprandial motor pattern. Motor patterns are defined in terms of properties such as contraction frequency and propagation. In a validation procedure using conventional manual analysis, the program correctly identifies the number of individual contractions with a 98% confidence interval and also correctly recognises 96% of phase 3 motor activity.

摘要

已开发出一种算法,用于对人体上段小肠的长时间测压记录进行离线分析。样本数据是在人体十二指肠和空肠中采集的,六个固态应变片式传感器安装在一根与便携式数字记录设备相连的硅导管上。数据以4赫兹的频率采样并进行滤波。为了进行精确计算,滤波后的信号被转换为四阶三次B样条函数。基于指数加权移动平均法,从信号中计算出基线。根据最小振幅和持续时间的阈值识别收缩。所开发的算法计算这些收缩的特性,如振幅、持续时间、面积和运动指数。此外,该程序能自动识别空腹人体小肠的正常运动模式,如移行性运动复合波,并有助于识别餐后运动模式。运动模式是根据收缩频率和传播等特性来定义的。在使用传统人工分析的验证过程中,该程序能以98%的置信区间正确识别单个收缩的数量,并且还能正确识别96%的第三相运动活动。

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