Sixth International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2009)
Snoring is the hallmark of the obstructive
sleep apnoea syndrome and several studies explore
possible correlations between them. In this work an
improved methodology with respect to  is proposed,
based on a proper energy threshold applied on audio
recordings for sound/silence detection, and on a
feature vector of 14 elements (13 mel frequency
cepstral coefficient plus the number of zero
crossings) for sound classification. This feature vector
is obtained from a 62-elements one by applying a
genetic algorithm, fitted to obtain the best
classification of the training/validation sets.
The feature vector is analyzed by means of a radial
basis neural network to perform snore events
identification. Finally, formant frequencies and time
analysis are also investigated to split up post-apnoeic
snores and normal ones.
Audio data from 26 patients of different age and sex are used to test the methodology: 6 patients (3 male and 3 female) were used to train the nets (1800 snores) and 4 patients to validate the classification (600 snores). On the whole dataset of patients, a sensitivity between 69% and 84% is obtained in the detection of post-apnoeic snores.
Index Terms. snore, neural network, Mel frequency cepstral coefficients, genetic algorithm, obstructive sleep apnoea
Full Paper (reprinted with permission from Firenze University Press)
Bibliographic reference. Calisti, M. / Bocchi, L. / Manfredi, Claudia / Romagnoli, I. / Gigliotti, F. / Donzelli, G. (2009): "Automatic detection of post-apnoeic snore events from home and clinical full night sleep recordings", In MAVEBA-2009, 189-192.