Automatic detection, segmentation and classification of snore related signals from overnight audio recording

Kun Qian; Zhiyong Xu; Huijie Xu; Yaqi Wu; Zhao Zhao

Automatic detection, segmentation and classification of snore related signals from overnight audio recording

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Author(s): Kun Qian ¹ ; Zhiyong Xu ¹ ; Huijie Xu ² ; Yaqi Wu ¹ ; Zhao Zhao ¹
- Affiliations: 1: School of Electronic and Optical Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei Street, Nanjing, People's Republic of China;
  2: Department of Otolaryngology, Beijing Hospital, No. 1, Dahua Road, Beijing, People's Republic of China
Source: Volume 9, Issue 1, February 2015, p. 21 – 29
DOI: 10.1049/iet-spr.2013.0266 , Print ISSN 1751-9675, Online ISSN 1751-9683

Received 03/07/2013, Accepted 15/05/2014, Revised 10/04/2014, Published 18/02/2015

Snore related signals (SRS) have been found to carry important information about the snore source and obstruction site in the upper airway of an Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) patient. An overnight audio recording of an individual subject is the preliminary and essential material for further study and diagnosis. Automatic detection, segmentation and classification of SRS from overnight audio recordings are significant in establishing a personal health database and in researching the area on a large scale. In this study, the authors focused on how to implement this intelligent method by combining acoustic signal processing with machine learning techniques. The authors proposed a systematic solution includes SRS events detection, classifier training, automatic segmentation and classification. An overnight audio recording of a severe OSAHS patient is taken as an example to demonstrate the feasibility of their method. Both the experimental data testing and subjective testing of 25 volunteers (17 males and 8 females) demonstrated that their method could be effective in automatic detection, segmentation and classification of the SRS from original audio recordings.

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