Automatic detection of post-apnoeic snore events from home and clinical full night sleep recordings

Calisti, M, Bocchi, L, Manfredi, C , Romagnoli, I, Gigliotti, F and Donzelli, G (2009) Automatic detection of post-apnoeic snore events from home and clinical full night sleep recordings. In: Models and Analysis of Vocal Emissions for Biomedical Applications - 6th International Workshop, MAVEBA, 2009.

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©2009 Firenze University Press. 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.

Keywords:Genetic algorithm, Mel frequency cepstral coefficients, Neural network, Obstructive sleep apnoea, Snore
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:46205
Deposited On:24 Aug 2021 14:40

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