title: Automatic snoring sounds detection from sleep sounds via multi-features analysis.
abstract: Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious respiratory disorder. Snoring is the most intuitively characteristic symptom of OSAHS. Recently, many studies have attempted to develop snore analysis technology for diagnosing OSAHS. The preliminary and essential step in such diagnosis is to automatically segment snoring sounds from original sleep sounds. This study presents an automatic snoring detection algorithm that detects potential snoring episodes using an adaptive effective-value threshold method, linear and nonlinear feature extraction using maximum power ratio, sum of positive/negative amplitudes, 500 Hz power ratio, spectral entropy (SE) and sample entropy (SampEn), and automatic snore/nonsnore classification using a support vector machine. The results show that SampEn provides higher classification accuracy than SE. Furthermore, the proposed automatic detection method achieved over 94.0% accuracy when identifying snoring and nonsnoring sounds despite using small training sets. The sensitivity and accuracy of the results demonstrate that the proposed snoring detection method can effectively classify snoring and nonsnoring sounds, thus enabling the automatic detection of snoring.
authors.affiliation: School of Physics and Optoelectronics, South China University of Technology, Guangzhou, 510640, China.
authors.forename: Can
authors.initials: C
authors.lastname: Wang
authors.affiliation: School of Physics and Optoelectronics, South China University of Technology, Guangzhou, 510640, China. phjxpeng@163.com.
authors.forename: Jianxin
authors.initials: J
authors.lastname: Peng
authors.affiliation: State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery, Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China.
authors.forename: Lijuan
authors.initials: L
authors.lastname: Song
authors.affiliation: State Key Laboratory of Respiratory Disease, Department of Otolaryngology-Head and Neck Surgery, Laboratory of ENT-HNS Disease, First Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510120, China. entxiaowen@163.com.
authors.forename: Xiaowen
authors.initials: X
authors.lastname: Zhang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001331:Automation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012890:Sleep / Q000502:physiology*; D012913:Snoring / Q000175:diagnosis*; D055815:Young Adult
keywords: Classification; Feature extraction; Obstructive sleep apnea hypopnea syndrome; Snoring detection