A Bag of Wavelet Features for Snore Sound Classification.
Bag-of-audio-words
Drug-induced sleep endoscopy
Obstructive sleep apnea
Snore sound
Wavelets
Journal
Annals of biomedical engineering
ISSN: 1573-9686
Titre abrégé: Ann Biomed Eng
Pays: United States
ID NLM: 0361512
Informations de publication
Date de publication:
Apr 2019
Apr 2019
Historique:
received:
18
06
2018
accepted:
21
01
2019
pubmed:
1
2
2019
medline:
27
6
2019
entrez:
1
2
2019
Statut:
ppublish
Résumé
Snore sound (SnS) classification can support a targeted surgical approach to sleep related breathing disorders. Using machine listening methods, we aim to find the location of obstruction and vibration within a subject's upper airway. Wavelet features have been demonstrated to be efficient in the recognition of SnSs in previous studies. In this work, we use a bag-of-audio-words approach to enhance the low-level wavelet features extracted from SnS data. A Naïve Bayes model was selected as the classifier based on its superiority in initial experiments. We use SnS data collected from 219 independent subjects under drug-induced sleep endoscopy performed at three medical centres. The unweighted average recall achieved by our proposed method is 69.4%, which significantly ([Formula: see text] one-tailed z-test) outperforms the official baseline (58.5%), and beats the winner (64.2%) of the INTERSPEECH COMPARE Challenge 2017 Snoring sub-challenge. In addition, the conventionally used features like formants, mel-scale frequency cepstral coefficients, subband energy ratios, spectral frequency features, and the features extracted by the OPENSMILE toolkit are compared with our proposed feature set. The experimental results demonstrate the effectiveness of the proposed method in SnS classification.
Identifiants
pubmed: 30701397
doi: 10.1007/s10439-019-02217-0
pii: 10.1007/s10439-019-02217-0
doi:
Types de publication
Clinical Trial
Comparative Study
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1000-1011Subventions
Organisme : European Union's Seventh Framework
ID : 338164
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