Sixth International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2009)

Florence, Italy
December 14-16, 2009

Automatic Detection of Post-Apnoeic Snore Events from Home and Clinical Full Night Sleep Recordings

M. Calisti (1), L. Bocchi (1), Claudia Manfredi (1), I. Romagnoli (2), F. Gigliotti (2), G. Donzelli (2)

(1) Department of Electronics and Telecommunications, Università degli Studi di Firenze, Italy
(2) Fondazione Don C. Gnocchi, Firenze, Italy
(3) Departments of Paediatrics, Università degli Studi di Firenze, Firenze, Italy

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 [4] 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.