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[Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing]
Hongqiang Mo1,2, Xiang Tian3, Bin Li1,2
1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. China.
Summary
Linear preprocessing enhances adaptive noise reduction in body sound auscultation by suppressing signal spikes. This method improves the performance of normalized least-mean-square (NLMS) filters for clearer body sound recordings.
Area of Science:
- Biomedical Engineering
- Signal Processing
Context:
- Adaptive filtering, particularly least-mean-square (LMS) methods, is crucial for ambient noise reduction in auscultation.
- Non-Gaussian body sounds with pulse components can cause weight misalignment in traditional adaptive filters.
Purpose:
- To introduce and analyze linear preprocessing as a method to improve the denoising performance of normalized least-mean-square (NLMS) adaptive filtering.
- To address the issue of weight misalignment in adaptive filters used for body sound auscultation.
Summary:
- Linear preprocessing was investigated for its ability to suppress spikes in body sounds, reducing their variance and power spectral density.
- This preprocessing step was shown to decrease weight misalignment in NLMS filters without significantly degrading ambient noise signals.
- The steady-state mean square weight deviation was found to be proportional to body sound variance and inversely proportional to ambient noise variance.
Impact:
- Significantly improved ambient noise reduction performance in body sound auscultation.
- Provides a design framework for adaptive denoising algorithms in medical auscultation applications, demonstrated with heart sound analysis.
