Abstract
Obstructive sleep apnea (OSA) is one of the most common breathing-related sleep disorders affecting individuals of different age groups, genders and origins. It is characterized by short-duration of cessations in breathing during sleep due to the collapse of the upper airway. The golden standard and reliable test for the detection of OSA is conducted by specialized physicians performing a polysomnographic sleep study. However, this test is time/labor consuming, expensive and cumbersome. In this paper, a non-invasive technique employing three different artificial neural networks to analyze spectral and statistical features of the Heart Rate Variability (HRV) signal to identify OSA subjects from normal control is investigated. The artificial networks include the single perceptron network, the feedforward network with back-propagation and the probabilistic neural network. The highest performance on MIT standard data is achieved by the feedforward network with back propagation using wavelet-based frequency domain features with specificity, sensitivity, and accuracy of 90%, 100% and 96.67%, respectively.
| Original language | English |
|---|---|
| Title of host publication | ISCAIE 2018 - 2018 IEEE Symposium on Computer Applications and Industrial Electronics |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 281-284 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781538635278 |
| DOIs | |
| Publication status | Published - Jul 5 2018 |
| Event | 2018 IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2018 - Penang Island, Malaysia Duration: Apr 28 2018 → Apr 29 2018 |
Other
| Other | 2018 IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2018 |
|---|---|
| Country | Malaysia |
| City | Penang Island |
| Period | 4/28/18 → 4/29/18 |
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Keywords
- ANN
- HRV
- OSA
- Time-Domain features
- Wavelet Packet Decomposition
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Computer Networks and Communications
- Computer Science Applications
- Electrical and Electronic Engineering
- Control and Optimization
- Instrumentation
Cite this
Different neural networks approaches for identification of obstructive sleep apnea. / Ali, Sarah Qasim; Hossen, Abdulnasir.
ISCAIE 2018 - 2018 IEEE Symposium on Computer Applications and Industrial Electronics. Institute of Electrical and Electronics Engineers Inc., 2018. p. 281-284.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Different neural networks approaches for identification of obstructive sleep apnea
AU - Ali, Sarah Qasim
AU - Hossen, Abdulnasir
PY - 2018/7/5
Y1 - 2018/7/5
N2 - Obstructive sleep apnea (OSA) is one of the most common breathing-related sleep disorders affecting individuals of different age groups, genders and origins. It is characterized by short-duration of cessations in breathing during sleep due to the collapse of the upper airway. The golden standard and reliable test for the detection of OSA is conducted by specialized physicians performing a polysomnographic sleep study. However, this test is time/labor consuming, expensive and cumbersome. In this paper, a non-invasive technique employing three different artificial neural networks to analyze spectral and statistical features of the Heart Rate Variability (HRV) signal to identify OSA subjects from normal control is investigated. The artificial networks include the single perceptron network, the feedforward network with back-propagation and the probabilistic neural network. The highest performance on MIT standard data is achieved by the feedforward network with back propagation using wavelet-based frequency domain features with specificity, sensitivity, and accuracy of 90%, 100% and 96.67%, respectively.
AB - Obstructive sleep apnea (OSA) is one of the most common breathing-related sleep disorders affecting individuals of different age groups, genders and origins. It is characterized by short-duration of cessations in breathing during sleep due to the collapse of the upper airway. The golden standard and reliable test for the detection of OSA is conducted by specialized physicians performing a polysomnographic sleep study. However, this test is time/labor consuming, expensive and cumbersome. In this paper, a non-invasive technique employing three different artificial neural networks to analyze spectral and statistical features of the Heart Rate Variability (HRV) signal to identify OSA subjects from normal control is investigated. The artificial networks include the single perceptron network, the feedforward network with back-propagation and the probabilistic neural network. The highest performance on MIT standard data is achieved by the feedforward network with back propagation using wavelet-based frequency domain features with specificity, sensitivity, and accuracy of 90%, 100% and 96.67%, respectively.
KW - ANN
KW - HRV
KW - OSA
KW - Time-Domain features
KW - Wavelet Packet Decomposition
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UR - http://www.scopus.com/inward/citedby.url?scp=85050690712&partnerID=8YFLogxK
U2 - 10.1109/ISCAIE.2018.8405485
DO - 10.1109/ISCAIE.2018.8405485
M3 - Conference contribution
AN - SCOPUS:85050690712
SP - 281
EP - 284
BT - ISCAIE 2018 - 2018 IEEE Symposium on Computer Applications and Industrial Electronics
PB - Institute of Electrical and Electronics Engineers Inc.
ER -