基于鼾声的声学特征估计睡眠呼吸暂停指数

doi:10.12066/j.issn.1007-2861.1942

上海大学学报(自然科学版) ›› 2019, Vol. 25 ›› Issue (4): 435-444.doi: 10.12066/j.issn.1007-2861.1942

• 研究论文 • 下一篇

基于鼾声的声学特征估计睡眠呼吸暂停指数

侯丽敏(), 张伟涛, 施丹, 刘焕成

上海大学通信与信息工程学院, 上海200444

收稿日期:2017-05-19 出版日期:2019-08-30 发布日期:2019-09-04
通讯作者: 侯丽敏 E-mail:lmhou@staff.shu.edu.cn
基金资助:
国家自然科学基金资助项目(61525203);国家自然科学基金资助项目(61572308);上海市科委基金资助项目(13441901600)

Estimation of apnea hypopnea index based on acoustic features of snoring

Limin HOU(), Weitao ZHANG, Dan SHI, Huancheng LIU

School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received:2017-05-19 Online:2019-08-30 Published:2019-09-04
Contact: Limin HOU E-mail:lmhou@staff.shu.edu.cn

摘要/Abstract

摘要：

睡眠呼吸暂停低通气综合征(sleep apnea hypopnea syndrome,SAHS)是一种睡眠呼吸疾病.提出用鼾声的声学特征对SAHS严重程度进行筛查的方法.提取鼾声的美频率倒谱系数(Mel-frequency cepstral coefficients,MFCC), 利用高斯混合模型对鼾声进行建模和分类,并估计了打鼾者的呼吸暂停低通气指数(apnea hypopnea index, AHI).对120人的实验结果表明, 与多导睡眠仪(polysomnography,PSG)诊断的AHI值相比,本方法对严重程度诊断的正确率达80.00${\%}$,与PSG诊断的一致性达到83.30${\%}$,相关系数为0.956 3 ($P<$0.001).说明MFCC是筛查SAHS较为有效的声学特征.研究结果对医疗辅助诊断和居家医疗的发展有着积极的促进作用.

关键词: 睡眠呼吸暂停低通气综合征, 鼾声, 美频率倒谱系数, 高斯混合模型, 呼吸暂停低通气指数

Abstract:

Sleep apnea hypopnea syndrome (SAHS) is a sleep respiratorydisorder. This paper proposes a method to screen severity of SAHSaccording to acoustic features of snoring. Mel-frequency cepstralcoefficients (MFCC) of snores are extracted and Gaussian mixturemodels (GMMs) used for snoring classification. Apnea hypopnea index(AHI) of the snorer is estimated. The results show that diagnosticaccuracy of SAHS severity is 80.00${\%}$, as compared topolysomnography (PSG). Agreement with PSG is 83.30${\%}$, and thePearson correlation coefficient is 0.956 3 ($P<$0.001). The resultssuggest that MFCC is a group of valid features for SAHS screening.This study has significance to the medical aided diagnosis of SAHS,and to the development of home health care.

Key words: sleep apnea hypopnea syndrome (SAHS), snore sound, Mel-frequency cepstral coefficients (MFCC), Gaussian mixture model, apnea hypopnea index (AHI)

中图分类号:

TP274

侯丽敏, 张伟涛, 施丹, 刘焕成. 基于鼾声的声学特征估计睡眠呼吸暂停指数[J]. 上海大学学报(自然科学版), 2019, 25(4): 435-444.

Limin HOU, Weitao ZHANG, Dan SHI, Huancheng LIU. Estimation of apnea hypopnea index based on acoustic features of snoring[J]. Journal of Shanghai University（Natural Science Edition）, 2019, 25(4): 435-444.

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基于鼾声的声学特征估计睡眠呼吸暂停指数

Estimation of apnea hypopnea index based on acoustic features of snoring

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