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Non-linear digital filters for extracting crackles from lung sounds
K Arakawa1, H Harashima, M Ono
1Department of Computer Science, Meiji University, Kawasaki, Japan.
Frontiers of Medical and Biological Engineering : the International Journal of the Japan Society of Medical Electronics and Biological Engineering
|January 1, 1991
Summary
A novel digital filter system effectively extracts crackles (discontinuous lung sounds) using two simple filters. This method accurately identifies these non-stationary respiratory sounds in lung sound data.
Area of Science:
- Digital signal processing
- Respiratory acoustics
- Biomedical engineering
Background:
- Crackles are discontinuous, paroxysmal sounds in lung sounds.
- Accurate detection of crackles is crucial for diagnosing respiratory conditions.
- Existing methods for crackle extraction can be complex or imprecise.
Purpose of the Study:
- To propose a non-linear digital filter system for automatic crackle extraction.
- To develop a system with simple filter designs for efficient processing.
- To achieve high performance in crackle extraction from lung sounds.
Main Methods:
- A two-filter system was designed: a stationary-non-stationary separating filter and a width separating filter.
- The stationary-non-stationary filter uses prediction error to isolate non-stationary signals, including crackles.
- The width-separating filter employs logical algebra based on zero-crossing intervals to extract crackle waveforms.
Main Results:
- The proposed system successfully separates non-stationary signals, which include crackles.
- The width-separating filter precisely extracts crackle signals from the non-stationary components.
- Both filters are realized with simple designs, contributing to system efficiency.
- High performance was demonstrated when processing actual lung sound data.
Conclusions:
- The developed non-linear digital filter system provides an effective method for automatic crackle extraction.
- The system's simplicity and high performance make it suitable for clinical applications.
- This approach offers a promising tool for the analysis of respiratory sounds.