Automatic classification of excitation location of snoring sounds | Zendy

Jingpeng Sun | Zendy; Xiyuan Hu | Zendy; Silong Peng | Zendy; ChungKang Peng | Zendy; Yan Ma | Zendy

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Automatic classification of excitation location of snoring sounds

Author(s) -

Jingpeng Sun,

Xiyuan Hu,

Silong Peng,

ChungKang Peng,

Yan Ma

Publication year - 2021

Publication title -

journal of clinical sleep medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.529

H-Index - 92

eISSN - 1550-9397

pISSN - 1550-9389

DOI - 10.5664/jcsm.9094

Subject(s) - obstructive sleep apnea , medicine , hypopnea , receiver operating characteristic , airway , epiglottis , sensitivity (control systems) , feature (linguistics) , artificial intelligence , principal component analysis , apnea , area under curve , audiology , pattern recognition (psychology) , polysomnography , speech recognition , surgery , anesthesia , computer science , engineering , larynx , linguistics , philosophy , electronic engineering , pharmacokinetics

For surgical treatment of patients with obstructive sleep apnea-hypopnea syndrome, it is crucial to locate accurately the obstructive sites in the upper airway; however, noninvasive methods for locating the obstructive sites have not been well explored. Snoring, as the cardinal symptom of obstructive sleep apnea-hypopnea syndrome, should contain information that reflects the state of the upper airway. Through the classification of snores produced at four different locations, this study aimed to test the hypothesis that snores generated by various obstructive sites differ.

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