Assess Sleep Stage by Modern Signal Processing Techniques

IEEE Transactions on Biomedical Engineering

Volumn 62, Issue 4, 2015, Pages 1159-1168

  Wu, Hau Tieng ^a   Talmon, Ronen ^b   Lo, Yu Lun ^c  

^a UNIVERSITY OF TORONTO   (Canada)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^c CHANG GUNG UNIVERSITY   (Taiwan)

Author keywords

breathing pattern variability; Empirical Intrinsic Geometry (EIG); Sleep Stage; Synchrosqueezing transform (ST)

Indexed keywords

ELECTROENCEPHALOGRAPHY; RADIAL BASIS FUNCTION NETWORKS; SIGNAL PROCESSING; SLEEP RESEARCH; SUPPORT VECTOR MACHINES;

ADAPTIVE SIGNAL PROCESSING TECHNIQUES; BREATHING PATTERNS; ELECTROENCEPHALOGRAPHIC SIGNALS; INTRINSIC GEOMETRY; MODERN SIGNAL PROCESSING; MULTI-CLASS SUPPORT VECTOR MACHINES; SLEEP STAGE; SYNCHROSQUEEZING;

BIOMEDICAL SIGNAL PROCESSING;

ADULT; ARTICLE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; ELECTROENCEPHALOGRAPHY; EMPIRICAL INTRINSIC GEOMETRY; FEMALE; FOURIER TRANSFORMATION; GOLD STANDARD; HUMAN; HUMAN EXPERIMENT; KERNEL METHOD; MALE; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MEASUREMENT ACCURACY; MEASUREMENT ERROR; NIGHT SLEEP; NORMAL HUMAN; PSYCHOPHYSIOLOGIC ASSESSMENT; REM SLEEP; SIGNAL PROCESSING; SLEEP STAGE; SUPPORT VECTOR MACHINE; SYNCHROSQUEEZING TRANSFORM; ALGORITHM; CLASSIFICATION; MIDDLE AGED; PHYSIOLOGY;

ADULT; ALGORITHMS; ELECTROENCEPHALOGRAPHY; FEMALE; HUMANS; MALE; MIDDLE AGED; SIGNAL PROCESSING, COMPUTER-ASSISTED; SLEEP STAGES;

EID: 84925847519     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2014.2375292     Document Type: Article

References (33)

1. T. Lee-Chiong, Sleep Medicine: Essentials and Review. London, U.K.: Oxford Univ. Press, 2008.
2. A. Rechtschaffen and A. Kales, A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects.Washington, DC, USA: Public Health Service, US Government Printing Office, 1968.
3. C. Iber et al., The AASM Manual for Scoring of Sleep and Associated Events-Rules: Terminology and Technical Specification. Darien, IL, USA: Amer. Acad. Sleep Med., 2007.
4. R. Norman et al., "Interobserver agreement among sleep scorers from different centers in a large dataset," Sleep, vol. 23, no. 7, pp. 901-908, 2000.
5. V. Bajaj and R. B. Pachori, "Automatic classification of sleep stages based on the time-frequency image of EEGsignals," Comput.Methods Programs Biomed., vol. 112, no. 3, pp. 320-328, 2013.
6. N.Kannathal et al., "Entropies for detection of epilepsy in EEG," Comput. Methods Programs Biomed., vol. 80, pp. 187-194, 2005.
7. S. Blanco et al., "Time-frequency analysis of electroencephalogram series,"Phys. Rev. E, vol. 51, no. 3, pp. 2624-2631, 1995.
8. S. Geng et al., "EEG non-linear feature extraction using correlation dimension and hurst exponent," Neurological Res., vol. 33, no. 9, pp. 908-912, 2011.
9. R. Talmon and R. Coifman, "Empirical intrinsic geometry for nonlinear modeling and time series filtering," Proc. Nat. Acad. Sci., vol. 110, no. 31, pp. 12 535-12 540, 2013.
10. R. Talmon and R. R. Coifman, "Intrinsic modeling of stochastic dynamical systems using empirical geometry," Appl. Comput. Harmon. Anal., 2014, Tech. Rep. YALEU/DCS/TR1467.
11. D. Duncan et al., "Identifying preseizure state in intracranial EEG data using diffusion kernels," Math. Biosci. Eng., vol. 10, pp. 579-590, 2013.
12. R. Talmon et al., "Manifold learning for latent variable inference in dynamical systems," Submitted, 2014, Tech. Report YALEU/DCS/TR1491.
13. th Int. Special Topic Conf. Inf. Technol. Appl. Biomed., 2007, pp. 194-197.
14. G. Guerrero-Mora et al., "Sleep-wake detection based on respiratory signal acquired through a pressure bed sensor," in Proc. Annu. Int. Conf. Eng. Med. Biol. Soc., 2012, pp. 3452-3455.
15. J. Sloboda and M. Das, "A simple sleep stage identification technique for incorporation in inexpensive electronic sleep screening devices," in Proc. IEEE Nat. Aerosp. Electron. Conf., 2011, pp. 21-24.
16. H.-T. Wu, "Instantaneous frequency and wave shape functions (I)," Appl. Comput. Harmon. Anal., vol. 35, pp. 181-199, 2013.
17. Y.-C. Chen et al., "Nonparametric and adaptive modeling of dynamic seasonality and trend with heteroscedastic and dependent errors," J. Roy. Stat. Soc. B, vol. 76, pp. 651-682, 2014.
18. R. Talmon et al., "Diffusion maps for signal processing: A deeper look at manifold-learning techniques based on kernels and graphs," IEEE Trans. Signal Process., vol. 30, no. 4, pp. 75-86, Jul. 2013.
19. I. Daubechies et al., "Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool," Appl. Comput. Harmon. Anal., vol. 30, pp. 243-261, 2011.
20. M. Engoren, "Approximate entropy of respiratory rate and tidal volume during weaning frommechanical ventilation," Critical CareMed., vol. 26, pp. 1817-1823, 1998.
21. G. Benchetrit, "Breathing pattern in humans: Diversity and individuality,"Respir. Physiol., vol. 122, no. 2/3, pp. 123-129, 2000.
22. M. Wysocki et al., "Reduced breathing variability as a predictor of unsuccessful patient separation from mechanical ventilation," Critical Care Med., vol. 34, pp. 2076-2083, 2006.
23. H.-T. Wu et al., "Evaluating physiological dynamics via synchrosqueezing: Prediction of ventilator weaning," IEEE Trans. Biomed. Eng., vol. 61, no. 3, pp. 736-744, Mar. 2013.
24. A. Singer and R. R. Coifman, "Non-linear independent component analysis with diffusion maps," Appl. Comput. Harmon. Anal., vol. 25, no. 2, pp. 226-239, 2008.
25. R. R. Coifman and S. Lafon, "Diffusion maps," Appl. Comput. Harmon. Anal., vol. 21, no. 1, pp. 5-30, 2006.
26. S. Mallat, "Group invariant scattering," Pure Appl. Math., vol. 10, no. 65, pp. 1331-1398, 2012.
27. J. Bruna et al., "Multiscale intermittent process analysis by scattering,"Submitted, arXiv:1311.4104, 2013.
28. M. Belkin and P. Niyogi, "Convergence of laplacian eigenmaps," in Proc. Conf. Adv. Neural Inf. Process. Syst. 19, 2007, vol. 19, p. 129.
29. A. Singer and H.-T.Wu, "Spectral convergence of the connection laplacian from random samples," Submitted, 2013.
30. N. El Karoui, "On information plus noise kernel random matrices," Ann. Stat., vol. 38, no. 5, pp. 3191-3216, 2010.
31. N. El Karoui and H.-T.Wu, "Connection graph Laplacian methods can be made robust to noise," Ann. Stat., 2014, accepted for Publication.
32. B. Scholkopf and A. Smola, Learning With Kernels. Cambridge, MA: MIT Press, 2002.
33. R. Rifkin and A. Klautau, "In defense of one-vs-all classification," J. Mach. Learn. Res., vol. 5, pp. 101-141, 2004.