Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Respiratory Medicine

Background:

• Automated respiratory disorder detection relies on breath sound analysis.
• Noisy breath sound signals compromise diagnostic accuracy in automated systems.
• A novel hybrid approach is proposed to mitigate noise in breath sound data.

Purpose of the Study:

• To develop and evaluate a hybrid method for effective noise reduction in respiratory sound signals.
• To improve the quality of breath sound data for enhanced diagnostic interpretation.
• To address the challenge of signal noise in computerized auscultation systems.

Main Methods:

• Recorded breath sounds from 80 patients with chronic obstructive pulmonary disease (COPD), 75 asthmatics, and 80 healthy individuals.
• Applied a hybrid noise reduction technique combining a Butterworth band-pass filter, transient artifact reduction, and spectral subtraction.
• Assessed noise rejection performance using metrics like Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR).

Main Results:

• The hybrid algorithm achieved a high Signal-to-Noise Ratio (SNR) of 70 dB.
• The algorithm demonstrated a Peak Signal-to-Noise Ratio (PSNR) of 72 dB.
• Effective noise suppression was confirmed across different breath sound categories.

Conclusions:

• The proposed hybrid method is highly effective in suppressing noise from breath sound signals.
• This technique can produce clean breath sound data suitable for diagnostic applications.
• The findings support the use of this method in automated respiratory diagnostic systems.