Name
Title | ||
|---|---|---|
Automatic detection of inspiration related snoring signals from original audio recording |
Abstract | ||
|---|---|---|
Inspiration related snoring signals (IRSS) are essential for doctors and researchers to develop further study and establishment of personal health database. How to detect IRSS automatically from original audio recording is significant in methods of acoustic based Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) diagnosis and monitoring. We proposed a systematic approach combining signal processing with machine learning techniques to detect IRSS from audio recording. Both the experimental results and computer studies demonstrate the efficiency of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ChinaSIP.2014.6889209 | ChinaSIP |
Keywords | Field | DocType |
inspiration related snoring signals, signal processing, machine learning, Obstructive Sleep Apnea/Hypopnea Syndrome | Signal processing,Obstructive sleep apnea,Computer science,Speech recognition,Sound recording and reproduction,Hypopnea,Personal health | Conference |
Citations | PageRank | References |
2 | 0.38 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Qian | 1 | 40 | 17.69 |
| zhiyong xu | 2 | 11 | 1.27 |
| Huijie Xu | 3 | 5 | 1.47 |
| Boon Poh Ng | 4 | 2 | 0.38 |