Auscultation of the abdomen, one of physical examinations, has been used as a traditional technique for evaluating bowel motility. For the last few decades, there have been various comparative studies on the BSs of normal subjects and those of patients with bowel dysfunction, such as irritable bowel syndrome [5, 6, 8], Crohn's disease , diabetes mellitus  and obstruction , and their results showed significant differences between the features obtained from BS signals according to the pathological condition of bowel motility. Also, several researchers recently have developed the de-noising, segmentation and feature extraction strategies of BS signals based on various signal-processing techniques [9–16, 19]. However, relatively few studies related to the quantitative estimation of bowel motility have been performed. Therefore, the goal of this study is to show the possibility of the quantitative estimation of the CTT, which has been used for the clinical assessment of bowel motility, by means of an ANN model and acoustical features.
BSs are produced from the movement of the intestinal contents, gas and fluid during peristalsis. Generally, normal 'very loud', 'gurgling' and 'rumbling' sounds are easily produced by a healthy bowel during an active stage of digestion. On the other hands, hyperactive BSs ('loud', 'high-pitched' and 'tinkling' sounds) might be caused by diarrhea or early intestinal obstruction, whereas hypoactive sounds (very diminished or absent sounds) are associated with bowel obstruction, paralytic ileus, torsion of the bowel or peritonitis that indicate diminished peristalsis [23, 24]. As considering these differences of sounds, several informative BS-features related to the pathological condition of the gastrointestinal tract have been reported: time-domain features, such as sound-to-sound interval (silence between BSs durations) [5, 6, 14], number of BSs by time interval [4, 6, 11, 14], sound index (sum of the BSs amplitude) [4, 7], median duration [11, 14, 16] and median acoustic intensity [11, 14] of BSs, and frequency-domain ones, such as main frequency of BSs [4, 11, 14, 16, 18].
Besides those features mentioned above, jitter and shimmer selected in our approach are measures of the fundamental frequency and amplitude cycle-to-cycle variations, respectively. They can represent the deviation or displacement of some aspect of the pitches of sounds in frequency- and time-domain, accordingly, they have been successfully used in a speaker verification, emotion expression, vocal or articular pathologies [20, 25–27]. In this study, the jitters and shimmers were significantly different between two groups (p < 0.01) and highly correlated with the CTTs (correlation coefficient from -0.68 to -0.79). These differences were thought to be related to the delayed peristalsis caused by the impaired vagus and hypogastric nerves of the SCI patients , and this aspect might produce relatively decreased perturbation of the pitches of BSs. Therefore, the jitter and shimmer considered in our method could be employed as a useful clinical parameter for the continuous monitoring of the bowel motility.
In relation to recording-duration issues of our approach, the BS signals were analyzed based on short-term analysis that deals with small-duration samples of the entire activity in fasted humans, coincided with [5–8, 18, 19], and all of the features were obtained from impulsive BSs due to the management difficulty of subject's immobilization during the test and huge data. On the contrary, many researchers believe that the period of BS monitoring should be longer over at least 2 hours since lasting/regularly-sustained (RS) BSs monitored during a full migrating motor complex (MMC) cycle are also associated with bowel motility [4, 10–16]. Unfortunately, even though the acoustical features obtained from the short-term recording of 10 minutes set in this study showed its feasibility, this strategy would raise a subject prolific of controversy related to the statistical reliability of the acquired samples. Therefore, in a future study we need to determine the appropriate recording period and to implement additional techniques for RS-BSs treatment as well as impulsive 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 . 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. When applying 18 feature-datasets used in this study into the regression model, the regression equation between the selected features and measured CTT was obtained as follows;
Also, the correlation coefficient, MAE and RMSE between the CTTs and eCTTs were 0.89, 12.4 and 18.4 hours, respectively. As a result, the estimation errors of the ANN model (MAE of 10.6 and RMSE of 14.6 hours) were relatively lower than those of the regression model, whereas the correlation coefficients of both methods were almost same. Consequently, these results showed that the neural-network-based approach attempted in this study could enhance its performance.
The limitations of this study are the small number of subjects, and the effects of the physiological characteristics of the subjects, such as their body mass index or severity of the spinal cord impairment were not considered in our algorithm. In a future study, we will apply our algorithm to a larger number of patients with various bowel dysfunctions, as well as to normal subjects, in order to enhance the accuracy of the estimated CTT. Also, we plan to develop supplementary signal processing techniques for effectively reducing frictional noise generated unavoidably between the skin and microphone as well as unwanted bio-signals.