Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: a bioacoustics application in gastrointestinal-motility monitoring

Comput Biol Med. 2007 Apr;37(4):438-62. doi: 10.1016/j.compbiomed.2006.08.013. Epub 2006 Oct 4.

Abstract

The current paper describes a wavelet-based method for long-term processing and analysis of gastrointestinal sounds (GIS). Windowing techniques are used to select sequential blocks of the prolonged multi-channel recordings and proceed to various wavelet-domain processing stages. De-noising, significant-activity detection, automated segmentation and extraction of summary curves are applied in an integrated mode, allowing for enhanced content manipulation and analysis. The proposed analysis scheme combines flexible long-term graphical representation tools, while maintaining the ability of quick browsing via visualization and auralization of the detected short-term events. This work is part of a project aiming to implement non-invasive diagnosis over gastrointestinal-motility (GIM) physiology. However, the proposed techniques might be applied to any study of long-term bioacoustics time series.

MeSH terms

  • Algorithms*
  • Artifacts
  • Circadian Rhythm / physiology
  • Computer Graphics
  • Fasting
  • Fractals*
  • Gastrointestinal Motility / physiology*
  • Humans
  • Mathematical Computing
  • Monitoring, Ambulatory*
  • Postprandial Period / physiology
  • Reference Values
  • Signal Processing, Computer-Assisted*
  • Software
  • Sound Spectrography*