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

Abstract

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)

CLC Number:

TP274

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.

Figures/Tables 9

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Estimation of apnea hypopnea index based on acoustic features of snoring

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