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Chairman：Hung-Chi Yang
Presenter：Yu-Kai Wang
Advisor：Yeou-Jiunn Chen
Date：2013.10.30
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Introduction
Paper review
Purposes
Materials and Methods
Future works
References
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Sleep
◦ Patterns and waves of EEG
 The two most important characteristics of EEG elements
 Frequency
 Amplitude
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The frequency range is divided into four bands
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Beta (12-30 Hz)
Alpha (8-12 Hz)
Theta (4-8 Hz)
Delta (0,1-4 Hz)
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Effective diagnosis and treatment of patients with sleep
Our objective is to utilize an classifier Using
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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
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Data acquisition
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The sleep recordings utilized are obtained from the
Sleep-EDF database ,from the PhysioBank
Eight full sleep recordings from Caucasian
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Aged from 21 to 35
Were not on any medication at the time of the
data collection
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Feature extraction
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First used six FIR bandpass filters
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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
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N
Energy   X i2
(1)
i 1
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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
SampEn=-log A/B
(2)
A = no of template vector pairs having d[X m (i),X m (j)] < r of length m+1
B = no of template vector pairs having d[X m (i),X m (j)] < r of length m
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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
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◦ To understand the essence of SVM classification, one
needs only to grasp four basic concepts
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The separating hyperplane
The maximum-margin hyperplane
The soft margin
The kernel function
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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
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[30] M.E.Tagluk,N.Sezgin,M.Akin,Estimationofsleepstagebyanartificial neyral
networkemployingEEG,EMG,andEOG,J.Med.Syst.34(4)(2010) 717–725.
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Thank You
For Your Attention
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