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Data driven filtering of bowel sounds using multivariate empirical mode decomposition.

Konstanze Kölle1,2, Muhammad Faisal Aftab3, Leif Erik Andersson3

  • 1Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. konstanze.koelle@ntnu.no.

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A novel filtering method using intrinsic mode function-fractal dimension (IMF-FD) effectively identifies bowel sounds in noisy abdominal recordings. This technique achieves near-perfect detection of gastro-intestinal signals, improving diagnostic accuracy.

Keywords:
Bowel soundsFractal dimensionMultivariate empirical mode decomposition

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Gastroenterology

Background:

  • Abdominal sound analysis aids in diagnosing gastro-intestinal diseases.
  • Bowel sounds, originating from the stomach and intestines, present as bursts and require differentiation from noise and artifacts in realistic recordings.

Purpose of the Study:

  • To develop and evaluate a novel filtering technique for enhancing and isolating bowel sounds from contaminated abdominal recordings.
  • To improve the accuracy of bowel sound detection in the presence of noise and artifacts.

Main Methods:

  • The intrinsic mode function-fractal dimension (IMF-FD) filtering method utilizes multivariate empirical mode decomposition (MEMD) to decompose abdominal signals into frequency components.
  • Information-carrying intrinsic mode functions (IMFs) are selected based on their fractal dimension (FD), and the filtered signal is reconstructed.
  • Heuristic rules are applied to eliminate artifacts within the same frequency range as bowel sounds.

Main Results:

  • The IMF-FD filter enhances non-linear components and segments them from the original signal.
  • The method demonstrates effective differentiation of bowel sounds from noise and artifacts.

Conclusions:

  • The IMF-FD filtering method shows promising performance on realistic, contaminated datasets.
  • The technique achieved close to 100% identification of manually labeled bowel sounds.