Characteristics of normal lung sounds after adaptive filtering

Area of Science:

• Pulmonary medicine
• Biomedical engineering
• Acoustics

Background:

• Lung sound analysis is crucial for diagnosing respiratory conditions.
• Heart sounds often contaminate lung sound recordings, complicating analysis.
• Digital signal processing offers potential for isolating lung sounds.

Purpose of the Study:

• To evaluate the effectiveness of digital filtering techniques in removing heart sounds from lung sound recordings.
• To determine the impact of filtering on the frequency spectrum of lung sounds.
• To obtain purer lung sound signals for improved diagnostic accuracy.

Main Methods:

• Lung sounds and electrocardiograms were recorded from five healthy male subjects.
• Simulated heart sounds were generated and digitally subtracted from lung sounds.
• Sound signals underwent band-pass filtering (25-1,000 Hz), digitization (3,000 Hz), and analysis using direct fast Fourier transform (NF), 75 Hz high-pass filtering (75 HzF), and adaptive filtering (AF).

Main Results:

• Both 75 HzF and AF significantly increased the mean, median, and mode frequencies of lung sounds compared to NF (p < 0.0001).
• AF resulted in lower mean, median, and mode frequencies than 75 HzF (p < 0.01).
• Filtering low-frequency heart sounds effectively shifted the lung sound frequency spectrum upward.

Conclusions:

• Digital filtering, particularly adaptive filtering, is effective in removing heart sound interference from lung sound recordings.
• Removing low-frequency artifacts enhances the clarity and diagnostic potential of lung sound analysis.
• These signal processing techniques can lead to more accurate characterization of lung acoustics.