number of access : ?
number of downloads : ?
ID 114993
Author
Sato, Ryunosuke Tokushima University
Gojima, Yuki Tokushima University
Keywords
bowel sound
bowel motility
automatic detection/evaluation
power-normalized cepstral coefficients
noncontact instrumentation
Content Type
Journal Article
Description
Information on bowel motility can be obtained via magnetic resonance imaging (MRI)s and X-ray imaging. However, these approaches require expensive medical instruments and are unsuitable for frequent monitoring. Bowel sounds (BS) can be conveniently obtained using electronic stethoscopes and have recently been employed for the evaluation of bowel motility. More recently, our group proposed a novel method to evaluate bowel motility on the basis of BS acquired using a noncontact microphone. However, the method required manually detecting BS in the sound recordings, and manual segmentation is inconvenient and time consuming. To address this issue, herein, we propose a new method to automatically evaluate bowel motility for noncontact sound recordings. Using simulations for the sound recordings obtained from 20 human participants, we showed that the proposed method achieves an accuracy of approximately 90% in automatic bowel sound detection when acoustic feature power-normalized cepstral coefficients are used as inputs to artificial neural networks. Furthermore, we showed that bowel motility can be evaluated based on the three acoustic features in the time domain extracted by our method: BS per minute, signal-to-noise ratio, and sound-to-sound interval. The proposed method has the potential to contribute towards the development of noncontact evaluation methods for bowel motility.
Journal Title
Applied Sciences
ISSN
20763417
Publisher
MDPI
Volume
8
Issue
6
Start Page
999
Published Date
2018-06-19
Rights
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology