7th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2011)
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. Keyword: snore, obstructive sleep apnea, neural network, automatic segmentation
Full Paper (reprinted with permission from Firenze University Press)
Bibliographic reference. Gritti, F. / Bocchi, L. / Romagnoli, I. / Gigliotti, F. / Manfredi, Claudia (2011): "An automatic and efficient method of snore events detection from sleep audio recordings", In MAVEBA-2011, 21-24.