Defecation Perception Reconstruction of an Artificial Anal Sphincter System

doi:10.16183/j.cnki.jsjtu.2019.222

Abstract

Abstract:

To overcome the shortcomings of data acquisition and analysis methods in the existing artificial anal sphincter system, a fecal perception reconstruction system is designed. The system mainly includes the design of a sensor module, a data acquisition and storage module, and a data analysis algorithm. Through the cooperation of the multi-sensor data acquisition and the storage module, the distribution of rectal surface pressure is reconstructed. A fecal perception prediction model based on principal components analysis (PCA) and support vector machines (SVM) is proposed (PCA-SVM). The results show that the axial and radial pressure signals of the upper arm and the radial position of the middle arm are significantly related to the fecal perception. The SVM algorithm based on the Gauss kernel function and the penalty factor C=0.0595 and the width σ=0.9536 pairs of effective pressure indicators are selected. Compared with the feed-forward neural network model, the vector model has a higher prediction accuracy and meets the requirements of the fecal perception function of the artificial anal sphincter system.

Key words: artificial anal sphincter, fecal perception reconstruction, principal component analysis, support vector machine (SVM)

CLC Number:

R314
TH39

ZHU Dong, JIANG Pingping, YAN Guozheng, WANG Zhiwu, HAN Ding, ZHAO Kai, HUA Fangfang, YAO Shengjian, DING Zifan, ZHOU Zerun. Defecation Perception Reconstruction of an Artificial Anal Sphincter System[J]. Journal of Shanghai Jiaotong University, 2020, 54(8): 771-777.

Figures/Tables 10

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前馈神经网络识别	测试样本数/个	预测正样本数/个	便意预测准确率/%
Pre 1	37	31	83.78
Pre 2	37	30	81.08
Pre 3	37	30	81.08
Pre 4	37	28	75.68
Pre 5	37	29	78.38
Pre 6	37	28	75.68
Pre 7	37	25	67.57
Pre 8	37	26	70.27
Pre 9	37	25	67.57
Pre 10	37	23	62.16

识别方式	测试样本数/个	预测正样本数/个	便意预测准确率/%
PCA-SVM	47	44	93.62
前馈神经网络Pre 1	37	31	83.78
前馈神经网络Pre 10	37	23	62.16