Spectral Classification of Oral and Nasal Snoring Sounds Using a Support Vector Machine
Tsuyoshi Mikami*, Yohichiro Kojima*, Kazuya Yonezawa**,
Masahito Yamamoto***, and Masashi Furukawa***
*Tomakomai National College of Technology, 443 Nishikioka, Tomakomai 059-1275, Japan
**Department of Clinical Research, National Hospital Organization Hakodate Hospital, 18-16 Kawaharacho, Hakodate 041-8512, Japan
***Graduate School of Information Science & Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan
Since oral breathing during sleep tends to make the upper airway more collapsible, loud snoring caused by oral breathing is found in many sleep apnea/hypopnea patients and should be detected in the earlier stage. But unfortunately we cannot know our own sleep condition or snoring. Thus, a simple method that can detect oral snoring makes it possible to become a useful technique to develop a home medical device. For such purpose, we adopt a Support Vector Machine (SVM) classifier so as to classify oral and nasal snoring sounds based on the spectral properties. Fifteen subjects are asked to simulate snoring with oral and nasal breath respectively and the sounds are recorded with a linear sound recorder. We adopted seven kernel functions (linear, polynomial, sigmoid, Gaussian, Laplacian, chisquare, and Kullback-Leibler) for SVM-based spectral classification. As a result, over 95% of snoring sounds are successfully classified under the various cross validation test.
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