Estimating a Sleep Apnea Hypopnea Index Based on the ERB Correlation Dimension of Snore Sounds

• Main Authors: Limin Hou (Author), Qiang Pan (Author), Hongliang Yi (Author), Dan Shi (Author), Xiaoyu Shi (Author), Shankai Yin (Author)
• Format: Book
• Published: Frontiers Media S.A., 2021-02-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2673-253X; 10.3389/fdgth.2020.613725
• Keywords: apnea hypopnea index; correlation dimension; non-linear acoustic characteristics; snore sound; sleep apnea hypopnea syndrome; Medicine; R; Public aspects of medicine; RA1-1270; Electronic computers. Computer science; QA75.5-76.95
• Source: Frontiers in Digital Health, Vol 2 (2021)

This paper proposes a new perspective of analyzing non-linear acoustic characteristics of the snore sounds. According to the ERB (Equivalent Rectangular Bandwidth) scale used in psychoacoustics, the ERB correlation dimension (ECD) of the snore sound was computed to feature different severity levels of sleep apnea hypopnea syndrome (SAHS). For the training group of 93 subjects, snore episodes were manually segmented and the ECD parameters of the snores were extracted, which established the gaussian mixture models (GMM). The nocturnal snore sound of the testing group of another 120 subjects was tested to detect SAHS snores, thus estimating the apnea hypopnea index (AHI), which is called AHIECD. Compared to the AHIPSG value of the gold standard polysomnography (PSG) diagnosis, the estimated AHIECD achieved an accuracy of 87.5% in diagnosis the SAHS severity levels. The results suggest that the ECD vectors can be effective parameters for screening SAHS.