1. Chairman ： Hung-Chi Yang Presenter ： Yu-Kai Wang Advisor ： Yeou-Jiunn Chen Date ： 2013.10.30 0

2.  Introduction  Paper review  Purposes  Materials and Methods  Future works  References 1

3.  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) 2

4.  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 3

5.  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 4

6.  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 5 (1)

7.  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 6 (2)

8.  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 7

9. ◦ 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 8

10.  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 9 Source:google image

11.  [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. 10

12.  [30] M.E.Tagluk,N.Sezgin,M.Akin,Estimationofsleepstagebyanartificial neyral 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. 11

13. Thank You For Your Attention 12