Abstract
BackgroundRadiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised.MethodsTwelve healthy males (age: 24.8 ± 2.7 years) and 6 patients with spinal cord injury (6 males, age: 55.3 ± 7.1 years) 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), which are highly correlated to the CTTs measured by the conventional method, were used as the features of the input vector for the BPNN.ResultsAs a results, both the jitters and shimmers of the normal subjects were relatively higher than those of the patients, whereas the CTTs of the normal subjects were relatively lower than those of the patients (p < 0.01). Also, through k-fold cross validation, the correlation coefficient and mean average error between the CTTs measured by a conventional radiograph and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively.ConclusionsThe jitter and shimmer of the BS signals generated during the peristalsis could be clinically useful for the discriminative parameters of bowel motility. Also, the devised algorithm showed good potential for the continuous monitoring and estimation of bowel motility, instead of conventional radiography, and thus, it could be used as a complementary tool for the non-invasive measurement of bowel motility.
Highlights
Radiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility
The high peak was classified as BGS since the kurtosis value of the corresponding segment was almost zero. These results show the performance of the modified iterative kurtosis-based detection (mIKD) algorithm used for selectively detecting inherent bowel sounds (BS) segments, despite the difference in the BGS level and in BSs amplitude and number
In order to determine the availability of the jitter and shimmer used in our algorithm, we compared the values of the selected features obtained from the normal subjects with those of the patients
Summary
Radiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. A non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised Radiological scoring methods such as the Barr and Blethyn scores [1] and colon transit time (CTT) [2,3], which operate by means of X-rays and magnetic resonance imaging (MRI), have generally been used for the assessment of bowel motility. The fractal-dimension analysis of BS signals [9,10,13], principal component analysis (PCA) [14], Weiner filtering [15] and hybrid expert system using hierarchical audio pattern recognition [16] have been tried to detect the informative feature of BS and evaluate the bowel motility via an auscultation These BSs are generated from the movement of the intestinal contents and gas in the lumen of the gastrointestinal tract during peristalsis; they can be used clinically as useful indicators of bowel motility
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