title: Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds.
abstract: BACKGROUND
  Radiological 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.
  
  
  METHODS
  Twelve 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.
  
  
  RESULTS
  As 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.
  
  
  CONCLUSIONS
  The 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.
authors.affiliation: School of Electronics and Information Engineering, Chonbuk National University, 664-14 1 Ga, Deokjin-dong, Jeonju, Republic of Korea.
authors.forename: Keo-Sik
authors.initials: KS
authors.lastname: Kim
authors.forename: Jeong-Hwan
authors.initials: JH
authors.lastname: Seo
authors.forename: Chul-Gyu
authors.initials: CG
authors.lastname: Song
mesh_terms: D000328:Adult; D000465:Algorithms; D001314:Auscultation; D003106:Colon / Q000502:physiology; D005769:Gastrointestinal Motility; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009142:Musculoskeletal Physiological Phenomena; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult