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Silence-breathing-snore classification from snore-related sounds
Asela S Karunajeewa1, Udantha R Abeyratne, Craig Hukins
1School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Brisbane, Australia.
Physiological Measurement
|February 8, 2008
Summary
Automated analysis of snore-related sounds (SRS) for obstructive sleep apnea (OSA) diagnosis is possible. A new algorithm achieved 96.78% accuracy in classifying snoring, breathing, and silence, even with background noise.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Sleep Medicine
Background:
- Obstructive sleep apnea (OSA) is a common condition with serious health implications.
- Snoring is an early symptom of OSA, but its diagnostic potential is underutilized.
- Accurate segmentation of snore-related sounds (SRS) into snoring, breathing, and silence is crucial for automated analysis.
Purpose of the Study:
- To present the classification performance of a novel pattern recognition-based segmentation algorithm for SRS.
- To evaluate the effectiveness of specific acoustic features in classifying SRS.
- To assess the impact of noise reduction techniques on algorithm performance.
Main Methods:
- Developed a segmentation algorithm using pattern recognition for SRS.
- Utilized four features: zero crossings, signal energy, normalized autocorrelation, and linear predictive coding (LPC) coefficients.
- Investigated the performance with and without three noise reduction (NR) techniques: amplitude spectral subtraction (ASS), power spectral subtraction (PSS), and short-time spectral amplitude (STSA) estimation.
Main Results:
- The algorithm achieved 90.74% accuracy using the four selected features.
- Incorporating NR techniques improved classification accuracy to 96.78%.
- Noise reduction significantly enhances the reliability of automated SRS analysis.
Conclusions:
- The developed algorithm shows promise for automated SRS analysis in OSA diagnosis.
- Feature selection and noise reduction are critical for high-accuracy classification.
- Automated analysis of SRS is a feasible approach for aiding OSA diagnosis.
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