Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

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A Comparative Study on Classification Methods of Sleep Stages by Using EEG

  • Kim, Jinwoo (Dept. of Information Communication Engineering, Kyungsung Univ.)
  • Received : 2013.12.13
  • Accepted : 2014.02.03
  • Published : 2014.02.28
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Abstract

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. In this paper, EEG signals have been analyzed using wavelet transform as well as discrete wavelet transform and classification using statistical classifiers such as euclidean and mahalanobis distance classifiers and a promising method SVM (Support Vector Machine). As a result of simulation, the average values of accuracies for the Linear Discriminant Analysis (LDA)-Quadratic, k-Nearest Neighbors (k-NN)-Euclidean, and Linear SVM were 48%, 34.2%, and 86%, respectively. The experimental results show that SVM classification method offer the better performance for reliable classification of the EEG signal in comparison with the other classification methods.

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References

  1. R.R Johnson, D.P. Popovic, R.E Olmstead, M. Stikic, D.J Levendowski, and C. Berka, "Drowsiness/Alertness Algorithm Development and Validation using Synchronized EEG and Cognitive Performance to Individualize a Generalized Model," Biol. Psychol., Vol. 87, No. 2, pp. 241-250, 2011. https://doi.org/10.1016/j.biopsycho.2011.03.003
  2. E.A. Schmidt, M. Schrauf, M. Simon, M. Fritzsche, A. Buchner, and W.E. Kincses, "Drivers' Misjudgement of Vigilance State during Prolonged Monotonous Daytime Driving," Accident Analysis and Prevention, Vol. 41, No. 5, pp. 1087-1093, 2009. https://doi.org/10.1016/j.aap.2009.06.007
  3. B.T Jap, S. Lal, and P. Fischer, "Comparing Combinations of EEG Activity in Train Drivers during Monotonous Driving," Expert Syst., Vol. 38, No. 1, pp. 996-1003, 2011. https://doi.org/10.1016/j.eswa.2010.07.109
  4. M.B. Kurt, N. Sezgin, M. Akin, G. Kirbas, and M. Bayram, "The ANN-based Computing of Drowsy Level," Expert Syst Appl., Vol. 36, No. 2, pp. 2534-2542, 2009. https://doi.org/10.1016/j.eswa.2008.01.085
  5. S.I. Shin, J.H. Cho, and M.N. Kim, "Proposition for 4 Channel Frontal Lobe Electrode Configuration and Study on EOG Removal from Measured EEG," J ournal of Korea Nultimedia Society, Vol. 6, No. 1, pp. 167-175, 2003.
  6. V.M. Yeo, X. Li, K. Shen, and E.P. V. Wider-Smith, "Can SVM be used for Automatic EEG Detection of Drowsiness during Car Driving?," Saf. Sci., Vol. 47, No. 1, pp. 115-124, 2009. https://doi.org/10.1016/j.ssci.2008.01.007
  7. U.R. Acharya, O. Faust, N. Kannathal, T. Chua, and S. Laxminarayan, "Non-linear Analysis of EEG Signals at Various Sleep Stages," Computer Methods Programs Biomed., Vol. 80, No. 1, pp. 37-45, 2005. https://doi.org/10.1016/j.cmpb.2005.06.011
  8. C.J. Stam, "Nonlinear Dynamical Analysis of EEG and MEG:Review of an Emerging Field," Clinical Neurophysiol., Vol. 116, No. 10, pp. 2266-2301, 2005. https://doi.org/10.1016/j.clinph.2005.06.011
  9. S.R. Liyanage, C. Guan, H. Zhang, K. Ang, J. Xu, and T.H. Lee, "Dynamically Weighted Ensemble Classification for Non-stationary EEG Processing," J. Neural Eng., Vol. 10, No. 3, pp. 1-10, 2013.
  10. Y. Yin, Y. Zhu, S. Xiong, and J. Zhang, "Drowsiness Detection from EEG Spectrum Analysis," Informatics in Control, Automation and Robotics, Vol. 133, No. 2012, pp. 753-759, 2012.
  11. C.T. Lin, K.C. Huang, C.H. Chuang, L.W. Ko, and T.P. Jung, "Can Arousing Feedback Rectify Lapses in Driving? Prediction from EEG Power Spectra," J. Neural Eng., Vol. 10, No. 5, pp. 1-10, 2013.
  12. M.K. Ahirwal and N.D londhe, "Power Spectrum Analysis of EEG Signals for Estimating Visual Attention," International Journal of Computer Applications, Vol. 42, No. 15, pp. 22-25, 2012.
  13. F.D. Carli, L. Nobili, P. Gelcich, and F. Ferrillo, "A Method For The Automatic Detection Of Arousals During Sleep," Sleep, Vol. 22, No. 5, pp. 561-572, 1999. https://doi.org/10.1093/sleep/22.5.561
  14. A. Kandaswamy, C.S. Kumar, R.P. Ramanathan, S. Jayaraman, and N. Malmurugan, "Neural Classification of Lung Sounds using Wavelet Coefficients," Computers in Biology and Medicine, Vol. 34, No. 6, pp. 523-537, 2004. https://doi.org/10.1016/S0010-4825(03)00092-1
  15. I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.
  16. O. Rioul and M. Vetterli, "Wavelets and Signal Processing," IEEE Sig. Proc. Magazine, Vol. 8, No. 4, pp. 14-38, 1991.
  17. W. Yan and W. Ren, "Use of Continuous-Wavelet Transmissibility for Structural Operational Modal Analysis," J . Struct. Eng., Vol. 139, No. 9, pp. 1444-1456, 2013. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000711
  18. V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, and K. Ramadoss, "Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform," Measurement Science Review, Vol. 6, No. 4, pp. 45-57, 2006.
  19. F. Protopapa, D. Mylonas, K. Spiliotis, C. Siettos, N. Smyrnis, and I. Evdokimidis, "Dynamic Analysis of EEG Signals during Spatial Working Memory used for Either Perception Discrimination or Planning of Action," Conf Proc IEEE Eng Med Biol Soc., pp. 5896-5899, 2011.
  20. H. Adeli, Z. Zhou, and N. Dadmehr, "Analysis of EEG Records in an Epileptic Patient using Wavelet Transform," J. Neurosci. Meth., Vol. 123, No. 1, pp. 69-87, 2003. https://doi.org/10.1016/S0165-0270(02)00340-0

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