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VMD-RiM: Rician Modeling of Temporal Feature Variation Extracted From Variational Mode Decomposed EEG Signal for Automatic Sleep Apnea Detection


VMD-RiM: Rician Modeling of Temporal Feature Variation Extracted From Variational Mode Decomposed EEG Signal for Automatic Sleep Apnea Detection

Bhattacharjee, Arnab, Fattah, Shaikh Anowarul, Zhu, Wei-Ping ORCID: https://orcid.org/0000-0001-7955-7044 and Ahmad, M. Omair ORCID: https://orcid.org/0000-0002-2924-6659 (2018) VMD-RiM: Rician Modeling of Temporal Feature Variation Extracted From Variational Mode Decomposed EEG Signal for Automatic Sleep Apnea Detection. IEEE Access, 6 . pp. 77440-77453. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2018.2883062


Electroencephalogram (EEG) is getting special attention of late in the detection of sleep apnea as it is directly related to the neural activity. But apnea detection through visual monitoring of EEG signal by an expert is expensive, difficult, and susceptible to human error. To counter this problem, an automatic apnea detection scheme is proposed in this paper using a single lead EEG signal, which can differentiate apnea patients and healthy subjects and also classify apnea and non-apnea frames in the data of an apnea patient. Each sub-frame of a given frame of EEG data is first decomposed into band-limited intrinsic mode functions (BLIMFs) by using the variational mode decomposition (VMD). The advantage of using VMD is to obtain compact BLIMFs with adaptive center frequencies, which give an opportunity to capture the local information corresponding to varying neural activity. Furthermore, by extracting features from each BLIMF, a temporal within-frame feature variation pattern is obtained for each mode. We propose to fit the resulting pattern with the Rician model (RiM) and utilize the fitted model parameters as features. The use of such VMD-RiM features not only offers better feature quality but also ensures very low feature dimension. In order to evaluate the performance of the proposed method, K nearest neighbor classifier is used and various cross-validation schemes are carried out. Detailed experimentation is carried out on several apnea and healthy subjects of various apnea-hypopnea indices from three publicly available datasets and it is found that the proposed method achieves superior classification performances in comparison to those obtained by the existing methods, in terms of sensitivity, specificity, and accuracy.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Authors:Bhattacharjee, Arnab and Fattah, Shaikh Anowarul and Zhu, Wei-Ping and Ahmad, M. Omair
Journal or Publication:IEEE Access
  • Concordia Open Access Author Fund
Digital Object Identifier (DOI):10.1109/ACCESS.2018.2883062
Keywords:EEG signal, entropy, goodness of feature, KNN classifier, model fitting, Rician model, sleep apnea, sub-framing, variational mode decomposition
ID Code:984891
Deposited On:16 Jan 2019 18:30
Last Modified:16 Jan 2019 18:30
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1. C. V. Senaratna et al., "Prevalence of obstructive sleep apnea in the general population: A systematic review", Sleep Med. Rev., vol. 34, pp. 70-81, Aug. 2017.

2. T. E. Weaver, C. F. George, Cognition and performance in patients with obstructive sleep apnea, Amsterdam, The Netherlands:Elsevier, 2010.

3. P. E. Peppard, T. Young, M. Palta, J. Skatrud, "Prospective study of the association between sleep-disordered breathing and hypertension", New England J. Med., vol. 342, pp. 1378-1384, 2000.

4. E. Shahar et al., "Sleep-disordered breathing and cardiovascular disease: Cross-sectional results of the sleep heart health study", Amer. J. Respiratory Crit. Care Med., vol. 163, no. 1, pp. 19-25, 2001.

5. M. Al-Angari, V. Sahakian, "Automated recognition of obstructive sleep apnea syndrome using support vector machine classifier", IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 3, pp. 463-468, May 2012.

6. J. A. Waxman, D. Graupe, D. W. Carley, "Automated prediction of apnea and hypopnea using a LAMSTAR artificial neural network", Amer. J. Respiratory Crit. Care Med., vol. 181, no. 7, pp. 727-733, 2010.

7. D. Alvarez, R. Hornero, J. V. Marcos, F. del Campo, M. Lopez, "Spectral analysis of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis", Proc. IEEE Annu. Int. Conf. Eng. Med. Biol. Soc. (EMBC), pp. 400-403, Sep. 2009.

8. D. Liu, Z. Pang, S. R. Lloyd, "A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG", IEEE Trans. Neural Netw., vol. 19, no. 2, pp. 308-318, Feb. 2008.

9. M. R. Azim, S. A. Haque, S. Amin, T. Latif, "Analysis of EEG and EMG signals for detection of sleep disordered breathing events", Proc. IEEE Int. Conf. Elect. Comput. Eng. (ICECE), pp. 646-649, Dec. 2010.

10. T. Schlüter, S. Conrad, "An approach for automatic sleep stage scoring and apnea-hypopnea detection", Frontiers Comput. Sci., vol. 6, no. 2, pp. 230-241, 2012.

11. J. Zhou, X.-M. Wu, W.-J. Zeng, "Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine", J. Clin. Monitor. Comput., vol. 29, no. 6, pp. 767-772, 2015.

12. R. Lin, R. Lee, C. Tseng, H. Zhou, C. Chao, J. Jiang, "A new approach for identifying sleep apnea syndrome using wavelet transform and neural networks", Biomed. Eng. Appl. Basis Commun., vol. 18, no. 3, pp. 138-143, 2006.

13. R.-G. Lee, C.-C. Chen, C.-C. Hsiao, H.-W. Wang, M.-S. Wei, "Sleep apnea syndrome recognition using the GreyART network", Biomed. Eng. Appl. Basis Commun., vol. 23, no. 3, pp. 163-172, 2011.

14. S. Taran, V. Bajaj, D. Sharma, "Robust Hermite decomposition algorithm for classification of sleep apnea EEG signals", Electron. Lett., vol. 53, no. 17, pp. 1182-1184, Jul. 2017.

15. A. Bhattacharjee, S. Saha, S. A. Fattah, W.-P. Zhu, M. O. Ahmad, "Sleep apnea detection based on Rician modeling of feature variation in multi-band EEG signal", IEEE J. Biomed. Health Inform..

16. W. S. Almuhammadi, K. A. I. Aboalayon, M. Faezipour, "Efficient obstructive sleep apnea classification based on EEG signals", Proc. IEEE Syst. Appl. Technol. Conf. (LISAT), pp. 1-6, May 2015.

17. S. Saha, A. Bhattacharjee, A. A. Ansary, S. A. Fattah, "An approach for automatic sleep apnea detection based on entropy of multi-band EEG signal", Proc. IEEE Region 10 Conf. (TENCON), pp. 420-423, Nov. 2016.

18. C. Shahnaz, A. T. Minhaz, S. T. Ahamed, "Sub-frame based apnea detection exploiting delta band power ratio extracted from EEG signals", Proc. IEEE Region 10 Conf. (TENCON), pp. 190-193, Nov. 2016.

19. F. Ahmed, P. Paromita, A. Bhattacharjee, S. Saha, S. Azad, S. Fattah, "Detection of sleep apnea using sub-frame based temporal variation of energy in beta band in EEG", Proc. IEEE Int. WIE Conf. Elect. Comput. Eng. (WIECON-ECE), pp. 258-261, Dec. 2016.

20. S. Taran, V. Bajaj, D. Sharma, "Teo separated AM-FM components for identification of apnea EEG signals", Proc. IEEE 2nd Int. Conf. Signal Image Process. (ICSIP), pp. 391-395, Aug. 2017.

21. M. E. Tagluk, N. Sezgin, "A new approach for estimation of obstructive sleep apnea syndrome", Expert Syst. Appl., vol. 38, no. 5, pp. 5346-5351, 2011.

22. C.-C. Hsu, P.-T. Shih, "A novel sleep apnea detection system in electroencephalogram using frequency variation", Expert Syst. Appl., vol. 38, no. 5, pp. 6014-6024, 2011.

23. K. Dragomiretskiy, D. Zosso, "Variational mode decomposition", IEEE Trans. Signal Process., vol. 62, no. 3, pp. 531-544, Feb. 2014.

24. F. L. da Silva, Niedermeyer’s Electroencephalography: Basic Principles Clinical Applications and Related Fields, Baltimore, MD, USA:Williams & Wilkins, 2005.

25. W. Klimesch, "EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis", Brain Res. Rev., vol. 29, no. 2, pp. 169-195, Apr. 1999.

26. C. Rotariu, C. Cristea, D. Arotaritei, R. G. Bozomitu, A. Pasarica, "Continuous respiratory monitoring device for detection of sleep apnea episodes", Proc. IEEE Int. Symp. Design Technol. Electron. Packag. (SIITME), pp. 106-109, Oct. 2016.

27. St. Vincent’s University Hospital/University College Dublin Sleep Apnea Database, Dec. 2018, [online] Available: http://www.physionet.org/physiobank/database/ucddb/.

28. MIT-BIH Polysomnographic Database, Dec. 2018, [online] Available: https://www.physionet.org/physiobank/database/slpdb/.

29. Sleep Recordings and Hypnograms in European Data Format (EDF)., Dec. 2018, [online] Available: https://physionet.org/pn4/sleepedfx/.
30. S. Chokroverty, Sleep Disorders Medicine: Basic Science Technical Considerations and Clinical Aspects, Oxford, U.K.:Butterworth-Heinemann, 2013.

31. J. Greene, "Feature subset selection using Thornton’s separability index and its applicability to a number of sparse proximity-based classifiers", Proc. Annu. Symp. Pattern Recognit. Assoc. South Africa, pp. 1-5, 2001.

32. J. Han, M. Kamber, J. Pei, Data Mining. Concepts and Techniques, Amsterdam, The Netherlands:Elsevier, 2011.

33. S. X. Moffett, S. M. O’Malley, S. Man, D. Hong, J. V. Martin, "Dynamics of high frequency brain activity", Sci. Rep., vol. 7, no. 1, pp. 15758, 2017.

34. T. Hori et al., "Proposed supplements and amendments to ‘A manual of standardized terminology techniques and scoring system for sleep stages of human subjects’ the Rechtschaffen & Kales (1968) standard", Psychiatry Clin. Neurosci., vol. 55, no. 3, pp. 305-310, 2001.

35. R. B. Berry et al., The AASM manual for the scoring of sleep and associated events, Darien, IL, USA:American Academy of Sleep Medicine, 2012.

36. W. R. Ruehland et al., "The 2007 AASM recommendations for EEG electrode placement in polysomnography: impact on sleep and cortical arousal scoring", Sleep, vol. 34, no. 1, pp. 73-81, 2011.
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