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Classification of the excitation location of snore sounds in the upper airway by acoustic multifeature analysis
- Publication Year :
- 2017
-
Abstract
- Objective: Obstructive sleep apnea (OSA) is a serious chronic disease and a risk factor for cardiovascular diseases. Snoring is a typical symptom of OSA patients. Knowledge of the origin of obstruction and vibration within the upper airways is essential for a targeted surgical approach. Aim of this paper is to systematically compare different acoustic features, and classifiers for their performance in the classification of the excitation location of snore sounds. Methods: Snore sounds from 40 male patients have been recorded during drug-induced sleep endoscopy, and categorized by Ear, Nose & Throat (ENT) experts. Crest Factor, fundamental frequency, spectral frequency features, subband energy ratio, mel-scale frequency cepstral coefficients, empirical mode decomposition-based features, and wavelet energy features have been extracted and fed into several classifiers. Using the ReliefF algorithm, features have been ranked and the selected feature subsets have been tested with the same classifiers. Results: A fusion of all features after a ReliefF feature selection step in combination with a random forests classifier showed the best classification results of 78% unweighted average recall by subject independent validation. Conclusion: Multifeature analysis is a promising means to help identify the anatomical mechanisms of snore sound generation in individual subjects. Significance: This paper describes a novel approach for the machine-based multifeature classification of the excitation location of snore sounds in the upper airway.
- Subjects :
- Adult
Male
Sound Spectrography
Speech recognition
Respiratory System
0206 medical engineering
Feature extraction
Biomedical Engineering
Feature selection
02 engineering and technology
Sensitivity and Specificity
Electronic mail
Pattern Recognition, Automated
Machine Learning
03 medical and health sciences
0302 clinical medicine
Wavelet
otorhinolaryngologic diseases
Humans
Medicine
Diagnosis, Computer-Assisted
Aged
Sleep Apnea, Obstructive
medicine.diagnostic_test
business.industry
Snoring
Reproducibility of Results
Sleep apnea
Auscultation
Middle Aged
medicine.disease
020601 biomedical engineering
respiratory tract diseases
Random forest
Mel-frequency cepstrum
ddc:004
business
Algorithms
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....a90d159f9ad32362649fb6be12fa57e6