Article Sidebar

Download
PDF
Statistic
Read Counter : 651 Download : 391

Downloads

Download data is not yet available.

Main Article Content

Abstract

The advancement of telecommunication technologies has provided us with new promising alternatives for remote diagnosis and possible treatment suggestions for patients of diverse health disorders, among which is the ability to identify Obstructive Sleep Apnea (OSA) syndrome by means of Electrocardiograph (ECG) signal analysis. In this paper, the standard spectral bands’ powers and statistical interval-based parameters of the Heart Rate Variability (HRV) signal were considered as a form of features for classifying the Sultan Qaboos University Hospital (SQUH) database for OSA syndrome into 4 different levels. Wavelet packet analysis was applied to obtain and estimate the standard frequency bands of the HRV signal. Further, the single perceptron neural network, the feedforward with back-propagation neural network and the probabilistic neural network have been implemented in the classification task. The classification between normal subjects versus severe OSA patients achieved 95% accuracy with the probabilistic neural network. While the classification between normal subjects versus mild OSA subjects reached accuracy of 95% also. When grouping mild, moderate and severe OSA subjects in one group compared to normal subjects as a second group, the classification with the feedforward network achieved an accuracy of 87.5%. Finally, when classifying subjects directly into one of the four classes (normal or mild or moderate or severe), a 77.5% accuracy was achieved with the feedforward network.

Keywords

Sleep Apnea Identification Classification HRV Wavelet Packet Decomposition Neural Networks.

Article Details

How to Cite
Hossen, A., & Qasim, S. (2020). IDENTIFICATION OF OBSTRUCTIVE SLEEP APNEA USING ARTIFICIAL NEURAL NETWORKS AND WAVELET PACKET DECOMPOSITION OF THE HRV SIGNAL. The Journal of Engineering Research [TJER], 17(1), 24–33. https://doi.org/10.24200/tjer.vol17iss1pp24-33
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Al Ghunaimi B (2003), Statistical Signal Characterization and Sub-band Decomposition for Heart Rate Variability Analysis in Patients with Obstructive Sleep Apnea (Masters Dissertation, Sultan Qaboos University).
  2. American Academy of Sleep Medicine (2001), International classification of sleep disorders, revised: Diagnostic and coding manual. Chicago, Illinois: American Academy of Sleep Medicine.
  3. DanTest Clinicians Team, “ANS Balance Assessments”. (2016). Sited from: https://www.dantest.com/dtr_ans_overview.htm.
  4. Global Leaders in Sleep and Respiratory Medicine. (2013), Sleep Apnea Facts and Figures. ResMed Corp. San Diego, CA, USA.
  5. Hagan M. T. Demuth, H.B. Beale M.H., and De Jesús O (1996), Neural Network Design (Vol. 20). Boston: PWS publishing company.
  6. Hamilton G., Chai-Coetzer C., (2019), “Update on the Assessment and Investigation of Adult Obstructive Sleep Apnea” Australian Journal of General Practice, 48 (4): 176-181.
  7. Kumar V.M. (2008), Sleep and sleep disorders. Indian Journal of Chest Diseases and Allied Sciences 50(1): 129.
  8. Lado M.J. Vila X.A. Rodríguez-Liñares L. Méndez A. J. Olivieri D. N. and Félix P (2011), Detecting sleep apnea by heart rate variability analysis: assessing the validity of databases and algorithms. Journal of Medical Systems 35(4): 473-481.
  9. Lee, W., Nagubadi, S., Kryger, M. H., & Mokhlesi, B. (2008). Epidemiology of Obstructive Sleep Apnea: a population-based perspective. Expert Review of Respiratory Medicine, 2(3), 349–364.
  10. Mietus, J E; Peng, C.K.; Henry, I.; Goldsmith, R.L.; Goldberger, A.L. (2002). “The pNNx files: re–examining a widely used heart rate variability measure”. Heart. 88: 378–380.
  12. Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J. M. (1996). Matlab Wavelet Toolbox User’s Guide. The MathWorks. Inc. Mass.
  13. Mohri M., Rostamizadeh A., Talwalkar A., (2012) Foundations of Machine Learning. The MIT Press ISBN 9780262018258
  14. Moore J. Heart Rate Variability Course (2016), “Heart Rate Variability vs. Heart Rate”, Sited from: https://hrvcourse.com.
  15. National Heart, Lung and Blood Institute. What Is Sleep Apnea?, (2012). Sited from: https://www.nhlbi.nih.gov/health/health-topics/ topics/sleepapnea
  16. National Sleep Foundation. (2016). “Sleep Apnea”. Sited from: https://sleepfoundation.org/sleep-disorders-problems/sleep-apnea
  17. Polikar, R. (2011). The Wavelet Tutorial Parts 1-3. Second Edition. Sited from: https://cseweb.ucsd.edu/baden/Doc/wavelets/polikar_wavelets.pdf.
  18. Sharma S., Raval M., Acharya U.R., 2019, A new approach to identify obstructive sleep apnea using an optimal orthogonal wavelet filter bank with ECG signals’ Informatics in Medicine Unlocked, 16, 100170: 1-13.
  19. Sysel, P., & Misurec, J. (2008). Estimation of power spectral density using wavelet thresholding. In S. Kartalopoulos, A. Buikis, N. Mastorakis, & L. Vladareanu (Eds.), WSEAS International Conference Proceedings Mathematics and Computers in Science and Engineering (No. 7). World Scientific and Engineering Academy and Society
  20. The University of Nottingham, Practice Learning Resources: Cardiology Teaching Package. (2017). http://www.nottingham.ac.uk/nursing/practice/resources/cardiology/function/normal_duration.pp
  21. UCDavis Health System. ‘Heart Rate’, (2016). Sited from:www.ucdmc.ucdavis.edu/sportsmedicine/resources/heart_rate_description.html
  22. WebMD, ‘Electrocardiogram’, (2013). Sited from: webmd.com/heartdisease/electrocardiogram/Wilson, S. (2013). Sleep Disorders (Oxford Psychiatry Library). 2nd ed. Oxford, United Kingdom: Oxford University Press, USA, p.1
  23. Xie, W., Zheng, F., & Song, X. (2014). Obstructive sleep apnea and serious adverse outcomes in patients with cardiovascular or cerebrovascular disease: a PRISMA-compliant systematic review and meta-analysis. Medicine, 93(29): 1-8.