Automatic classification of excitation location of snoring sounds
Sun JP(孙井鹏)1,2,3
发表期刊Journal of Clinical Sleep Medicine
2021-05-01
卷号17期号:5页码:1031-1038
摘要

Study Objectives: 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.
Methods: We trained and tested our model on a public data set that comprised 219 participants. For each snore episode, an acoustic and a physiological feature were extracted and concatenated, forming a 59-dimensional fusion feature. A principal component analysis and a support machine vector were used for dimensional reduction and snore classification. The performance of the proposed model was evaluated using several metrics: sensitivity, precision, specificity, area under the receiver operating characteristic curve, and F1 score.
Results: The unweighted average values of sensitivity, precision, specificity, area under the curve, and F1 were 86.36%, 89.09%, 96.4%, 87.9%, and 87.63%, respectively. The model achieved 98.04%, 80.56%, 72.73%, and 94.12% sensitivity for types V (velum), O (oropharyngeal), T (tongue), and E (epiglottis) snores.
Conclusions: The characteristics of snores are related to the state of the upper airway. The machine-learning–based model can be used to locate the vibration sites in the upper airway

关键词machine learning multiscale entropy snore classification obstructive sleep apnea hypopnea syndrome
收录类别SCI
语种英语
WOS记录号WOS:000660335600021
七大方向——子方向分类人工智能+医疗
引用统计
被引频次:6[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/44941
专题智能制造技术与系统研究中心_多维数据分析
作者单位1.Institute of Automation, Chinese Academy of Sciences, Beijing, People’s Republic of China
2.University of Chinese Academy of Sciences, Beijing, People’s Republic of China
3.Division of Interdisciplinary Medicine and Biotechnology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
第一作者单位中国科学院自动化研究所
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Sun JP. Automatic classification of excitation location of snoring sounds[J]. Journal of Clinical Sleep Medicine,2021,17(5):1031-1038.
APA Sun JP.(2021).Automatic classification of excitation location of snoring sounds.Journal of Clinical Sleep Medicine,17(5),1031-1038.
MLA Sun JP."Automatic classification of excitation location of snoring sounds".Journal of Clinical Sleep Medicine 17.5(2021):1031-1038.
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