Automatic classification of sleep stages based on the time-frequency image of EEG signals

Computer Methods and Programs in Biomedicine

Volumn 112, Issue 3, 2013, Pages 320-328

  Bajaj, Varun ^a   Pachori, Ram Bilas ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY INDORE   (India)

Author keywords

Automatic sleep stage classification; Electroencephalogram (EEG) signal; Image processing; Multiclass least squares support vector machines; Smoothed pseudo Wigner Ville distribution; Time frequency analysis

Indexed keywords

ELECTROENCEPHALOGRAM SIGNALS; LEAST SQUARES SUPPORT VECTOR MACHINES; SLEEP STAGE; SMOOTHED PSEUDO WIGNER-VILLE DISTRIBUTIONS; TIME FREQUENCY ANALYSIS;

FACE RECOGNITION; IMAGE PROCESSING; RADIAL BASIS FUNCTION NETWORKS; SLEEP RESEARCH; SUPPORT VECTOR MACHINES; WIGNER-VILLE DISTRIBUTION;

ELECTROENCEPHALOGRAPHY;

ADULT; ARTICLE; AUTOMATION; ELECTROENCEPHALOGRAM; FEMALE; HISTOGRAM; HUMAN; HUMAN EXPERIMENT; IMAGE PROCESSING; KERNEL METHOD; MALE; NORMAL HUMAN; SIGNAL PROCESSING; SLEEP STAGE; SUPPORT VECTOR MACHINE; WAVEFORM;

AUTOMATIC SLEEP STAGE CLASSIFICATION; ELECTROENCEPHALOGRAM (EEG) SIGNAL; IMAGE PROCESSING; MULTICLASS LEAST SQUARES SUPPORT VECTOR MACHINES; SMOOTHED PSEUDO WIGNER-VILLE DISTRIBUTION; TIME-FREQUENCY ANALYSIS;

AUTOMATION; ELECTROENCEPHALOGRAPHY; HUMANS; SLEEP;

EID: 84885418140     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2013.07.006     Document Type: Article

References (34)

1. Rechtschaffen A., Kales A. A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects 1968, Public Health Service, U.S. Govern. Printing Office, Washington, DC.
2. Iber C., Ancoli-Isreal S., Chesson A.L., Quan S.F. The AASM Manual for Scoring of Sleep and Associated Events-Rules: Terminology and Technical Specification 2007, American Academy of Sleep Medicine.
3. Collop N.A. Scoring variability between polysomnography technologists in different sleep laboratories. Sleep Medicine 2002, 3:43-47.
4. Norman R.G., Pal I., Stewart C., Walsleben J.A., Rapoport D.M. Interobserver agreement among sleep scorers from different centers in a large dataset. Sleep 2000, 23(7):901-908.
5. Pereda E., Gamundi A., Rial R., Gonzalez J. Non-linear behaviour of human EEG: fractal exponent versus correlation dimension in awake and sleep stages. Neuroscience Letters 1998, 250:91-94.
6. Chouvara I., Rosso V., Mendez M.O., Bianchi A.M., Parrino L., Grassi A., Terzano M., Cerutti S. Assessment of the EEG complexity during activations from sleep. Computer Methods and Programs in Biomedicine 2011, 104:e16-e28.
7. Acharya U.R., Faust O., Kannathal N., Chua T., Laxminarayan S. Non-linear analysis of EEG signals at various sleep stages. Computer Methods and Programs in Biomedicine 2005, 80:37-45.
8. Weiss B., Clemens Z., Bódizs R., Halász P. Comparison of fractal and power spectral EEG features: Effects of topography and sleep stages. Brain Research Bulletin 2011, 84:359-375.
9. Doroshenkov L.G., Konyshev V.A., Selishchev S.V. Classification of human sleep stages based on EEG processing using hidden Markov models. BioMedical Engineering 2007, 41(1):24-28.
10. Güneş S., Polat K., Yosunkaya Ş. Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Systems with Applications 2010, 37:7922-7928.
11. Vural C., Yildiz M. Detemination of sleep stage separation ability of features extracted from EEG signals using principle component anslysis. Journal of Medical Systems 2010, 34:83-89.
12. Fraiwan L., Lweesy K., Khasawneh N., Fraiwan M., Wenz H., Dickhaus H. Classification of sleep stages using multi-wavelet time-frequency entropy and LDA. Methods of Information in Medicine 2010, 230-237.
13. Sinha R.K. Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. Journal of Medical Systems 2008, 32:291-299.
14. Liang S.F., Kuo C.E., Hu Y.H., Pan Y.H., Wang Y.H. Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models. IEEE Transactions on Instrumentation and Measurement 2012, 61(6):1649-1657.
15. Hsu Y.L., Yang Y.T., Wang J.S., Hsu C.Y. Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing 2013, 104:105-114.
16. Koley B., Dey D. An ensemble system for automatic sleep stage classification using single channel EEG signal. Computers in Biology and Medicine 2012, 42:1186-1195.
17. Fraiwan L., Lweesy K., Khasawneh N., Wenz H., Dickhaus H. Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Computer Methods and Programs in Biomedicine 2012, 108:10-19.
18. van Sweden B., Kemp B., Kamphuisen H.A.C., van der Velde E.A. Alternative electrode placement in (automatic) sleep scoring (Fpz-Cz/Pz-Oz versus C4-A1/C3-A2). Sleep 1990, 13(3):279-283.
19. Mourtazaev M.S., Kemp B., Zwinderman A.H., Kamphuisen H.A.C. Age and gender affect different characteristics of slow waves in the sleep EEG. Sleep 1995, 18(7):557-564.
20. Kemp B., Zwinderman A.H., Tuk B., Kamphuisen H.A.C., Oberyé J.J.L. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical Engineering 2000, 47(9):1185-1194.
21. Pachori R.B., Sircar P. A new technique to reduce cross terms in the Wigner distribution. Digital Signal Processing 2007, 17:466-474.
22. Tzallas A.T., Tsipouras M.G., Fotiadis D.I. Automatic seizure detection bassed on time-frequency analysis and artificial neural networks. Computational Intelligence and Neuroscience 2007, Article ID 80510.
23. Auger F., Flandrin P., Goncalves P., Lemoine O. Time-frequency toolbox, CNRS (France) 1995-1996, Rice University, USA.
24. Allen R.L., Mills D.W. Signal Analysis: Time, Frequency, Scale, and Struture 2004, Wiley-IEEE Press, NY, USA.
25. Boashash B., Boubchir L., Azemi G. A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals. EURASIP Journal on Advances in Signal Processing 2012, 117.
26. Bajaj V., Pachori R.B. Separation of rhythms of EEG signals based on Hilbert-Huang transformation with application to seizure detection. ICHIT 2012. LNCS 7425 2012, 493-500. Springer-Verlag, Berlin Heidelberg. G. Lee (Ed.).
27. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979, 9(1):62-66.
28. Liu X., Wang D. Texture classification using spectral histograms. IEEE Transactions on Image Processing 2003, 12:661-670.
29. Vapnik V. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York.
30. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999, 9(3):293-300.
31. Bajaj V., Pachori R.B. Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Transactions on Information Technology in Biomedicine 2012, 16(6):1135-1142.
32. Suykens J.A.K., Vandewalle J. Multiclass least squares support vector machines. Proc. of the International Joint Conference on Neural Networks 1999, 900-903.
33. Xing Y., Wu X., Xu Z. Multiclass least squares wavelet support vector machines. IEEE International Conference on Networking, Sensing and Control 2008, 498-502.
34. Zavar M., Rahati S., Akbarzadeh-T M.-R., Ghasemifard H. Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection. Expert Systems with Applications 2011, 38:10751-10758.