Tracking and time-frequency analysis on nonlinearity of tracheal sounds

Area of Science:

• Biomedical Engineering
• Signal Processing
• Acoustics

Background:

• Tracheal sound (TS) analysis is crucial for respiratory diagnostics.
• Nonlinear characteristics in biological sounds can indicate pathological conditions.
• Existing methods for TS nonlinearity detection are limited.

Purpose of the Study:

• To identify and quantify nonlinear portions within tracheal sound signals.
• To develop and apply a novel nonlinear analysis method in the time-frequency domain.
• To evaluate the effectiveness of the proposed method on real tracheal sound recordings, including those contaminated by heart sounds.

Main Methods:

• Utilized third-order cumulant for initial identification of nonlinearities in tracheal sound.
• Applied a novel nonlinear analysis technique based on optimally weighted Wigner-Ville distributions.
• Employed a filter bank for subband signal processing.
• Performed similarity measurements between optimally weighted and unweighted time-frequency distributions for quantitative evaluation.

Main Results:

• Successfully identified nonlinear portions in tracheal sound signals.
• The novel time-frequency analysis provided quantitative measures of nonlinearity.
• The method demonstrated effectiveness on both preprocessed and real-world, heart sound-contaminated tracheal sound recordings.

Conclusions:

• Third-order cumulant is effective for detecting nonlinearity in tracheal sound.
• The proposed optimally weighted Wigner-Ville distribution method offers a robust approach for nonlinear analysis of tracheal sound.
• This technique can be applied to real-world respiratory sound analysis, even in the presence of noise like heart sounds.