Estimation Algorithm of Bowel Motility Based on Regression Analysis of the Jitter and Shimmer of Bowel Sounds

Abstract

-Bowel sounds (BS) are produced by the movement of the intestinal contents in the lumen of the gastro-intestinal tract during peristalsis and thus, it can be used clinically as useful indicators of bowel motility. We devised an estimation algorithm of bowel motility based on the regression modeling of the jitter and shimmer of BS signals measured by auscultation. Ten healthy males (23.5±2.1 years) were examined. Consequently, the correlation coefficient, coefficient of determination and standard error between the colon transit times (CTT) measured by a conventional radiograph and the values estimated by our algorithm were 0.98, 0.96 and 2.86, respectively. Also, through k-fold cross validation, the average value of the absolute differences between them was 5.0±2.5 hours. This method could be used as a complementary tool for the non-invasive measurement of bowel motility.

Automated Bowel Sound Analysis: An Overview

“…Interesting work on BS analysis was done by Kim et al who conducted a regression analysis of BS shimmer and jitter. They demonstrated a strict correlation with radiographic colon transit time in a preliminary study [ 68 ] and proposed a back-propagation neural network model of BS [ 69 ]. Kim et al underscored the potential of the method to non-invasively monitor bowel motility [ 70 ].…”

Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds

“…Recently, the estimation method based on the regression analysis between the features obtained from BS signals, such as the jitter and shimmer, and conventional CTT was implemented in [ 17 ]. Accordingly, the comparison of the estimation results of the proposed approach by means of the ANN model with those obtained from the previous regression model was performed.…”

“…Figure 2 shows the procedure used for detecting selectively the BS segments from noisy BS signals and extracting the features, in order to estimate the CTT using the ANN model. First, the recorded BS signals were processed using the modified iterative kurtosis-based detection (mIKD) algorithm, devised in our previous study [ 17 ], for the selective detection of BS segments through noise-gating. Significant deviations from kurtosis value can be attributed to the presence of non-Gaussian signals such as the BS, since kurtosis is theoretically zero for Gaussian signals such as back-ground sound (BGSs) signals [ 19 ].…”

“…Therefore, the aim of this study is to develop a non-invasive estimation algorithm of bowel motility, based on an artificial neural network (ANN) model of the jitter and shimmer, which were considered as useful features in recent study [ 17 ], of the BS signals during the digestive process. Also, we derived an ANN model between the acoustical features obtained from the BS signals and measured CTT, and finally determined the feasibility of the proposed method.…”