Abstract
Obstructive Sleep Apnea (OSA) is a common sleep disorder affecting $>10\%$ of the middle-aged population. The gold standard diagnostic procedure is the Polysomnography (PSG), which is both costly and time consuming. A simple and non-expensive screening therefore would be of great value. This study presents a novel at-home screening method for OSA using a smartphone, a microphone and a modified armband, to measure continuous biological signals during a whole night sleep. A signal-processing algorithm was used to classify the subjects, into classes according to severity of the disorder. The system was validated by conducting a routine sleep study parallel to the data acquisition on a total of 23 subjects. Both binary and 4-class classification problems were tested. The binary classifications showed the best results with sensitiv- ities between 92.3 % and 100 %, and accuracies between 78.3 % and 91.3 %. The 4-class classification was not as successful with a sensitivity of 75 %, and accuracies of 56.5 % and 60 %. We conclude that mobile smartphone technology has a potential for OSA ambulatory screening.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
| Publisher | IEEE |
| Publication date | 2018 |
| Pages | 457-460 |
| ISBN (Print) | 9781538636466 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Hawaii Convention Center, Honolulu, United States Duration: 17 Jul 2018 → 21 Jul 2018 Conference number: 40 https://embc.embs.org/2018/ |
Conference
| Conference | 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
|---|---|
| Number | 40 |
| Location | Hawaii Convention Center |
| Country/Territory | United States |
| City | Honolulu |
| Period | 17/07/2018 → 21/07/2018 |
| Internet address |
Keywords
- Sleep apnea
- Accelerometers
- Support vector machines
- Event detection
- Correlation
- Sensitivity
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