Chairman ： Hung-Chi Yang Presenter ： Yu-Kai Wang Advisor ： Yeou-Jiunn Chen Date ：

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

 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

 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

 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

 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)

 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)

 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

◦ 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

 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

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