Abstract
A novel and robust non-invasive method for identification of patients with obstructive sleep apnea (OSA) is introduced in this paper. Most sleep clinics suffer from the large number of patients with snoring who think that they have OSA and needs full overnight polysomnography. Thus, a need for simple non-invasive methods is of great importance to screen this large number of patients before the polysomnography. The method used in this paper depends on the continuous-wavelet transform and to be considered one of the methods with high-identification efficiency. A clear advantage of this method is its consistency with changing the wavelet-filter type. The data used in this paper are downloaded from MIT data bases. 60 subjects (40 OSA and 20 normal) are to be divided equally for both training and testing cycles.
| Original language | English |
|---|---|
| Title of host publication | ISCAIE 2017 - 2017 IEEE Symposium on Computer Applications and Industrial Electronics |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 22-27 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509047529 |
| DOIs | |
| Publication status | Published - Oct 18 2017 |
| Event | 2017 IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2017 - Langkawi Island, Malaysia Duration: Apr 24 2017 → Apr 25 2017 |
Other
| Other | 2017 IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2017 |
|---|---|
| Country | Malaysia |
| City | Langkawi Island |
| Period | 4/24/17 → 4/25/17 |
Fingerprint
Keywords
- Continuous Wavelet Transform
- Entropy
- Histogram
- Identification
- Obstructive Sleep Apnea
ASJC Scopus subject areas
- Instrumentation
- Computer Science Applications
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering