title: Snoring sound classification from respiratory signal.
abstract: Snoring is common in the general population and the irregularity could lead to the presence of Obstructive sleep apnea. Diagnosis of OSA could therefore be made by snoring sound analysis. However, there is still a shortage of robust methods to automatically detect snoring sounds without the need to calibrate for every individual. In this paper, a novel method based on neural network is proposed to classify breathing sound episodes from snoring and non-snoring sound segments. Our snore detection algorithm was applied to the tracheal sounds of nine individuals with different OSA severities. On the testing dataset, the classifier achieved a sensitivity and specificity of 95.9% and 97.6% respectively. Our results indicate that using such a method could help to detect snoring sounds with high accuracy which would be useful in the diagnosis of sleep apnea.
authors.forename: Mehrnaz
authors.initials: M
authors.lastname: Shokrollahi
authors.forename: Shumit
authors.initials: S
authors.lastname: Saha
authors.forename: Peyman
authors.initials: P
authors.lastname: Hadi
authors.forename: Frank
authors.initials: F
authors.lastname: Rudzicz
authors.forename: Azadeh
authors.initials: A
authors.lastname: Yadollahi
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D017286:Polysomnography; D015203:Reproducibility of Results; D012119:Respiration; D012135:Respiratory Sounds / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000503:physiopathology; D020181:Sleep Apnea, Obstructive / Q000503:physiopathology; D012913:Snoring / Q000175:diagnosis* / Q000503:physiopathology*; D014132:Trachea / Q000503:physiopathology; D055815:Young Adult