Amplitude spectrum trend-based feature for excitation location classification from snore sounds
文献类型：期刊论文
| 作者 | Sun, Jingpeng1 ; Hu, Xiyuan2; Chen, Chen1; Peng, Silong1; Ma, Yan3
|
| 刊名 | PHYSIOLOGICAL MEASUREMENT
 |
| 出版日期 | 2020-08-01 |
| 卷号 | 41期号:8页码:13 |
| 关键词 | amplitude trend signal decomposition null space pursuit snore classification OSA |
| ISSN号 | 0967-3334 |
| DOI | 10.1088/1361-6579/abaa34 |
| 通讯作者 | Hu, Xiyuan(xiyuan.hu@foxmail.com) |
| 英文摘要 | 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. |
| WOS关键词 | OBSTRUCTIVE SLEEP-APNEA ; CARDIOVASCULAR-DISEASE ; RISK-FACTOR ; NASENDOSCOPY ; SITE |
| 资助项目 | National Natural Science Foundation of China[61571438] |
| WOS研究方向 | Biophysics ; Engineering ; Physiology |
| 语种 | 英语 |
| WOS记录号 | WOS:000570483400001 |
| 出版者 | IOP PUBLISHING LTD |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/41970]  |
| 专题 | 自动化研究所_智能制造技术与系统研究中心_多维数据分析团队 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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