基于听觉子带关联维数的SAHS鼾声筛查

doi:10.3969/j.issn.0255-8297.2017.02.005

Abstract

Abstract:

This paper proposes a method for screening sleep apnea hypopnea syndrome (SAHS) by recording snore sound. Based on the equivalent rectangular bandwidth (ERB) scale used in psychoacoustics, the ERB correlation dimension (ECD) of snore is used to analyze and classify snores of different severity levels. For the training group, snore episodes were manually segmented and ECD features of snores were extracted to establish a Gaussian mixture model (GMM). The nocturnal snoring sound of the test group was validated to detect SAHS snores. The apnea hypopnea index (AHI) was estimated, to determine severity of SAHS. AHI is an average number of apneic events per hour of sleep. Compared with the polysomnography diagnosis that is a gold-standard, accuracy of SAHS severity reached 90%. The proposed method is useful in accessory examination of SAHS severity and for home uses.

Key words: sleep apnea hypopnea syndrome (SAHS), Gaussian mixture model, snore, correlation dimension

CLC Number:

TP274

HOU Li-min, SHI Dan, LIU Huan-cheng, ZHANG Wei-tao. Screening of SAHS Snore Based on ERB Correlation Dimension[J]. Journal of Applied Sciences, 2017, 35(2): 181-192.

References

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Screening of SAHS Snore Based on ERB Correlation Dimension

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