Name
Title | ||
|---|---|---|
Automatic detection, segmentation and classification of snore related signals from overnight audio recording |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1049/iet-spr.2013.0266 | IET Signal Processing |
Keywords | Field | DocType |
acoustic signal processing,audio recording,health care,learning (artificial intelligence),medical signal detection,medical signal processing,signal classification,osahs,srs events detection,automatic snore related signal classification,automatic snore related signal detection,automatic snore related signal segmentation,classifier training,machine learning techniques,obstructive sleep apnea-hypopnea syndrome patient,overnight audio recording,personal health database,snore obstruction site,snore source site | Signal processing,Experimental data,Segmentation,Speech recognition,Classifier (linguistics),Sound recording and reproduction,Mathematics,Hypopnea,Personal health | Journal |
Volume | Issue | ISSN |
9 | 1 | 1751-9675 |
Citations | PageRank | References |
3 | 0.42 | 7 |
Authors | ||
5 | ||