Neural Classification of Snoring Sounds for the Detection of Oral Breathing during Snoring

Tsuyoshi Mikami, Yohichiro Kojima, Masahito Yamamoto, and Masashi Furukawa


Intelligent Instrumentation, Biomedical Signal Processing, Neural Networks, Pattern Classification


Healthy people are generally breathing through nose during sleep, but oral breathing will be given rise to if the nasal cavity is gradually being closed. Nasal closure or even nasal congestion leads to open mouth during snoring, which causes the tongue base collapse, the origin of OSAS. Thus, if a simple home device with only a microphone can automatically monitor our snores at bedside and detect oral breathing during snoring, we can perceive an abnormality in our sleep condition easily and early detection and treatment of OSAS will be possible. In our previous work, we proposed some feature extraction methods for the stationary subsequences extracted from oral, nasal, and oronasal snoring sounds and analyzed their acoustic properties in detail. This paper addresses a snoring sound classification based on breathing route during snoring using Multilayer Perceptron, Support Vector Machines, and k-Nearest Neighbor method. According to our experiments, the SVMwith Gaussian kernel acquires the best performance where 82.5% of the oral, 89.2% of the nasal, and 73.6 % of the oronasal snoring sounds are successfully classified.

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