Advances in Acoustic Signal Processing Techniques for Enhanced Bowel Sound Analysis

With the invention of the electronic stethoscope and other similar recording and data logging devices, acoustic signal processing concepts and methods can now be applied to bowel sounds. In this paper, the literature pertaining to acoustic signal processing for bowel sound analysis is reviewed and d...

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Bibliographic Details
Published in:IEEE reviews in biomedical engineering 2019, Vol.12, p.240-253
Main Authors: Allwood, Gary, Du, Xuhao, Webberley, K. Mary, Osseiran, Adam, Marshall, Barry James
Format: Article
Language:English
Subjects:
Acoustic noise
Acoustic signal processing
Acoustics
Artificial intelligence
bowel sound
Data logging
Data processing
Digital signal processors
Electronic devices
Feature extraction
Frequency-domain analysis
Intestine
Learning algorithms
Localization
Logging
Machine learning
Medical instruments
Noise reduction
Recording
Segmentation
Signal processing
Sonar
Sound
Time-domain analysis
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Summary:With the invention of the electronic stethoscope and other similar recording and data logging devices, acoustic signal processing concepts and methods can now be applied to bowel sounds. In this paper, the literature pertaining to acoustic signal processing for bowel sound analysis is reviewed and discussed. The paper outlines some of the fundamental approaches and machine learning principles that may be used in bowel sound analysis. The advances in signal processing techniques that have allowed useful information to be obtained from bowel sounds from a historical perspective are provided. The document specifically address the progress in bowel sound analysis, such as improved noise reduction, segmentation, signal enhancement, feature extraction, localization of sounds, and machine learning techniques. We have found that advanced acoustic signal processing incorporating novel machine learning methods and artificial intelligence can lead to better interpretation of acoustic information emanating from the bowel.
ISSN:1937-3333
1941-1189
DOI:10.1109/RBME.2018.2874037