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Automatic sleep stage using energy features of EEG signals

Automatic sleep stage using energy features of EEG signals . Chairman ： Hung-Chi Yang Presenter ： Yu-Kai Wang Advisor ： Yeou-Jiunn Chen Date ： 2013.10.30. Outline. Introduction Paper review Purposes Materials and Methods Future works References. Introduction. Sleep

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Automatic sleep stage using energy features of EEG signals

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  1. Automatic sleep stage using energy features of EEG signals Chairman：Hung-Chi YangPresenter：Yu-Kai Wang Advisor：Yeou-Jiunn ChenDate：2013.10.30

  2. Outline • Introduction • Paper review • Purposes • Materials and Methods • Future works • References

  3. Introduction • Sleep • Patterns and waves of EEG • The two most important characteristics of EEG elements • Frequency • Amplitude • The frequency range is divided into four bands • Beta (12-30 Hz) • Alpha (8-12 Hz) • Theta (4-8 Hz) • Delta (0,1-4 Hz)

  4. Purposes • Effective diagnosis and treatment of patients with sleep • Our objective is to utilize an classifier Using • Energy • Entropy • Frequency band • GMM • Features extracted from EEG characteristic waves • To develop an effective automatic sleep stage classification system using only a single EEG channel

  5. Material and Methods • Data acquisition • The sleep recordings utilized are obtained from the Sleep-EDF database ,from the PhysioBank • Eight full sleep recordings from Caucasian • Aged from 21 to 35 • Were not on any medication at the time of the data collection

  6. Material and Methods • Feature extraction • First used six FIR bandpass filters • There are a total of 3000 samples in each characteristic wave in each 30s epoch • The sampling rate of EEG signals equals 100 Hz • EEG signals from each 30s epoch by using (1)

  7. Material and Methods • Sample-entropy • This is the rate of new information producted in a dynamic system • The negative natural logarithm of the conditional probability • Two sequences similar for m points would remain similar at the next point • A lower value of SaEn indicates • More self-similarity in the time series (2)

  8. Material and Methods • Support Vector Machines (SVM) • SVM is a supervised learning method • Classification • Regression • Training of SVM is to find the optimal hyperplane(thick solid line) • Separates the samples from two classes (circles vs. squares) with maximum margin

  9. Material and Methods • To understand the essence of SVM classification, one needs only to grasp four basic concepts • The separating hyperplane • The maximum-margin hyperplane • The soft margin • The kernel function

  10. Material and Methods • Neaural Network • Using Matlab toolbox (nntool) • Import Input data(train data) • Target data(stage) • Sample data(test data) • Create newnetwork(Feed-forward backprop) • Set training epochs(3000 epochs) • Simulation • Performance Source:google image

  11. References • [1] R. Agarwal, J. Gotman, Computer-assisted sleep staging,, IEEE Trans. Biomed. Eng. 48 (12) (2001) 1412–1423. • [2] S. Aydin, H.M. Sarao˘glu, S. Kara, Singular spectrum analysis of sleep EEG in insomnia, J. Med. Syst. 35 (4) (2011) 457–461. • [3] C. Berthomier, X. Drouot, M. Herman-Stoı¨ca, P. Berthomier, J. Prado, D. Bpkar- Thire, O. Benoit, J. Mattout, M. d’Ortho, Automatic analysis of single-channel sleep EEG: validation in healthy individuals, Sleep 30 (11) (2007) 1587–1595. • [4] A.G. Correa, E. Laciar, H.D. Patin˜ o, M.E. Valentinuzzi, An automatic sleep-stage classifier using electroencephalographic signals, Int. J. Med. Sci. 1 (1) (2008) 13–21. • [5] S. Charbonnier, L. Zoubek, S. Lesecq, F. Chapotot, Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging, Comput. Biol. Med. 41 (6) (2011) 380–389. • [6] K.I. Funahashi, Y. Nakamura, Approximation of dynamical systems by con- tinuous time recurrent neural networks,, Neural Networks 6 (6) (1993) 801–806. • [7] L.A. Feldkamp, G.V. Puskorius, A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification, Proc. IEEE 86 (11) (1998) 2259–2277.

  12. References • [30] M.E.Tagluk,N.Sezgin,M.Akin,Estimationofsleepstagebyanartificialneyral networkemployingEEG,EMG,andEOG,J.Med.Syst.34(4)(2010) 717–725. • [31] J.S.Wang,C.S.G.Lee,Self-adaptiveneuro-fuzzyinferencesystemsforclassi- fication applications,,IEEETrans.FuzzySyst.10(6)(2002)790–802. • [32] L.Zoubek,S.Charbonnier,S.Lesecq,A.Buguet,F.Chapotot,Featureselection for sleep/wakestagesclassificationusingdatadrivenmethods,,Biomed. Signal Process.Control2(3)(2007)171–179.

  13. Thank You For Your Attention

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