Enhancing bowel sounds by using a higher order statistics-based radial basis function network

Bor-Shyh Lin, Ming Jen Sheu, Ching Chin Chuang, Kuan Chih Tseng, Jen Yin Chen

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Auscultation of bowel sounds provides a noninvasive method to the diagnosis of gastrointestinal motility diseases. However, bowel sounds can be easily contaminated by background noises, and the frequency band of bowel sounds is easily overlapped with background noise. Therefore, it is difficult to enhance the noisy bowel sounds by using precise digital filters. In this study, a higher order statistics (HOS)-based radial basis function (RBF) network was proposed to enhance noisy bowel sounds. An HOS technique provides the ability of suppressing Gaussian noises and symmetrically distributed non-Gaussian noises due to their natural tolerance. Therefore, the influence of additional noises on the HOS-based learning algorithm can be reduced effectively. The simulated and experimental results show that the HOS-based RBF can exactly provide better performance for enhancing bowel sounds under stationary and nonstationary Gaussian noises. Therefore, the HOS-based RBF can be considered as a good approach for enhancing noisy bowel sounds.

Original languageEnglish
Pages (from-to)675-680
Number of pages6
JournalIEEE Journal of Biomedical and Health Informatics
Volume17
Issue number3
DOIs
StatePublished - 14 Oct 2013

Keywords

  • Bowel sound
  • Gaussian noise
  • Higher order statistics (HOS)
  • Radial basis function (RBF) network

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