Enhancing Bowel Sounds by Using a Higher Order Statistics-Based Radial Basis Function Network

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.