Awareness system for bowel motility estimation based on artificial neural network of bowel sounds

Awareness system of bowel motility estimation based on an artificial neural network (ANN) model of bowel sounds obtained by an auscultation was devised. Twelve healthy males and 6 patients with delayed bowel motility were examined. BS signals generated during the digestive process were recorded from 3 colonic segments (ascending, descending and sigmoid colon), and then, the acoustical features (jitter and shimmer) of the individual BS segment were obtained. Only 6 features (J1,3, J3,3, S1,2, S2,1, S2,2, S3,2) highly correlated to the conventional colon transit time (CTT) were used as the features. Through k-fold cross validation, the correlation coefficient and mean average error between the CTTs and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively. The devised system showed good potential for the continuous monitoring and estimating the bowel motility, instead of conventional radiography, and thus, it could be used as an awareness tool for the non-invasive measurement of bowel motility.