Amplitude spectrum trend-based feature for excitation location classification from snore sounds
Sun, Jingpeng1; Hu, Xiyuan2; Chen, Chen1; Peng, Silong1; Ma, Yan3
发表期刊PHYSIOLOGICAL MEASUREMENT
ISSN0967-3334
2020-08-01
卷号41期号:8页码:13
摘要

Objective: Successful surgical treatment of obstructive sleep apnea (OSA) depends on the precise location of the vibrating tissue. Snoring is the main symptom of OSA and can be utilized to detect the active location of tissues. However, existing approaches are limited, owing to their inability to capture the characteristics of snoring produced from the upper airway. This paper proposes a new approach to better distinguish different snoring sounds that are generated from four different excitation locations.Approach: First, we propose a robust null space pursuit algorithm for extracting the trend from the amplitude spectrum of snoring. Second, a new feature from this extracted amplitude spectrum trend, which outperforms the Mel-frequency cepstral coefficient (MFCC) feature, is designed. Subsequently, the newly proposed feature, namely the trend-based MFCC (TCC), is reduced in dimensionality by using principal component analysis. Finally, a support vector machine is employed for the classification task.Main results: By using the TCC, the proposed approach achieves an unweighted average recall of 87.5% on the classification of four excitation locations on the public dataset Munich Passau Snore Sound Corpus.Significance: The TCC is a promising feature for capturing the characteristics of snoring. The proposed method can effectively perform snore classification and assist in accurate OSA diagnosis.

关键词amplitude trend signal decomposition null space pursuit snore classification OSA
DOI10.1088/1361-6579/abaa34
关键词[WOS]OBSTRUCTIVE SLEEP-APNEA ; CARDIOVASCULAR-DISEASE ; RISK-FACTOR ; NASENDOSCOPY ; SITE
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61571438]
项目资助者National Natural Science Foundation of China
WOS研究方向Biophysics ; Engineering ; Physiology
WOS类目Biophysics ; Engineering, Biomedical ; Physiology
WOS记录号WOS:000570483400001
出版者IOP PUBLISHING LTD
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/173211/41971
专题智能制造技术与系统研究中心_多维数据分析
通讯作者Hu, Xiyuan
作者单位1.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
3.Harvard Med Sch, Ctr Dynam Biomarkers, Div Interdisciplinary Med & Biotechnol, Beth Israel Deaconess Med Ctr, Boston, MA 02215 USA
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Sun, Jingpeng,Hu, Xiyuan,Chen, Chen,et al. Amplitude spectrum trend-based feature for excitation location classification from snore sounds[J]. PHYSIOLOGICAL MEASUREMENT,2020,41(8):13.
APA Sun, Jingpeng,Hu, Xiyuan,Chen, Chen,Peng, Silong,&Ma, Yan.(2020).Amplitude spectrum trend-based feature for excitation location classification from snore sounds.PHYSIOLOGICAL MEASUREMENT,41(8),13.
MLA Sun, Jingpeng,et al."Amplitude spectrum trend-based feature for excitation location classification from snore sounds".PHYSIOLOGICAL MEASUREMENT 41.8(2020):13.
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