An automatic and efficient method of snore events detection from sleep audio recordings

F. Gritti, L. Bocchi, I. Romagnoli, F. Gigliotti, C. Manfredi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

snores are respiratory sounds produced during sleep. they are reported to be a risk factor for various sleep disorders, such as obstructive sleep apnea syndrome (OSA). diagnosis of OSA relies on the expertise of the clinician that inspects whole night polysomnographic recording. this inspection is time consuming and uncomfortable for the patients. thus, there is a strong need for a tool to analyze snore sounds automatically. nocturnal respiratory sounds are composed of two kind of events: "silence" episodes and "sound" episodes that include breathing, snoring and "other" sounds. in this paper a new method to detect snoring episodes from full night audio recordings is proposed. signal analysis is performed in three steps: Pre-processing, automatic segmentation, extraction of features and classification. With the segmentation step, only the "sound" parts of the audio signal are extracted using a short-term energy and the otsu thresholding method. the aim of classification step is the detection of snore episodes only, using two neural artificial network applied to four features (length, maximum amplitude, standard deviation and energy). data from 24 subject are analyzed using the proposed method; on the dataset, a sensitivity of 86,2% and specificity of 86,3% are obtained.

Original languageEnglish
Title of host publicationModels and Analysis of Vocal Emissions for Biomedical Applications - 7th International Workshop, MAVEBA 2011
PublisherFirenze University Press
Pages21-24
Number of pages4
ISBN (Print)9788866550099
Publication statusPublished - 2011
Event7th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2011 - Firenze, Italy
Duration: Aug 25 2011Aug 27 2011

Other

Other7th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2011
CountryItaly
CityFirenze
Period8/25/118/27/11

Fingerprint

Audio recordings
Acoustic waves
Signal analysis
Sleep
Inspection
Neural networks
Processing

Keywords

  • Automatic segmentation
  • Neural network
  • Obstructive sleep apnea
  • Snore

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

Cite this

Gritti, F., Bocchi, L., Romagnoli, I., Gigliotti, F., & Manfredi, C. (2011). An automatic and efficient method of snore events detection from sleep audio recordings. In Models and Analysis of Vocal Emissions for Biomedical Applications - 7th International Workshop, MAVEBA 2011 (pp. 21-24). Firenze University Press.

An automatic and efficient method of snore events detection from sleep audio recordings. / Gritti, F.; Bocchi, L.; Romagnoli, I.; Gigliotti, F.; Manfredi, C.

Models and Analysis of Vocal Emissions for Biomedical Applications - 7th International Workshop, MAVEBA 2011. Firenze University Press, 2011. p. 21-24.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gritti, F, Bocchi, L, Romagnoli, I, Gigliotti, F & Manfredi, C 2011, An automatic and efficient method of snore events detection from sleep audio recordings. in Models and Analysis of Vocal Emissions for Biomedical Applications - 7th International Workshop, MAVEBA 2011. Firenze University Press, pp. 21-24, 7th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2011, Firenze, Italy, 8/25/11.
Gritti F, Bocchi L, Romagnoli I, Gigliotti F, Manfredi C. An automatic and efficient method of snore events detection from sleep audio recordings. In Models and Analysis of Vocal Emissions for Biomedical Applications - 7th International Workshop, MAVEBA 2011. Firenze University Press. 2011. p. 21-24
Gritti, F. ; Bocchi, L. ; Romagnoli, I. ; Gigliotti, F. ; Manfredi, C. / An automatic and efficient method of snore events detection from sleep audio recordings. Models and Analysis of Vocal Emissions for Biomedical Applications - 7th International Workshop, MAVEBA 2011. Firenze University Press, 2011. pp. 21-24
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