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沈祖望Tsu-Wang Shen1 劉芳芷Hsin-Fang Li1 陳紹祖William Shao-Tsu
Chen2
1 慈濟大學醫學資訊學系 Department of Medical Informatics, Tzu Chi
University
2 花蓮慈濟醫院身心醫學科 Department of Psychiatry, Buddhist TzuChi General Hospital
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Presenter: Tzu-Yu Huang
Advisor: Dr. Yen-Ting Chen
Date: 12.29.2010
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An artificial neural network (ANN)
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Artificial intelligence
Mathematical model
Learning system
Computer-aid diagnosis
An artificial neural network (ANN):類神經網路
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Back propagation neural network (BPNN)
◦ Supervised neural network
XH
ih
W
a
1
WhjHY
3
4
1
b
c
3
L
d
1
2
3
4
5
 f
Output layer
4
3
L
k
2
Input layer
 f
2
k

H
h
e
 Yj
Hidden layer
Back propagation neural network (BPNN):倒傳遞類神經網路

Back propagation neural network (BPNN)
◦ Input-to-hidden
 Weight
nethH   h WihXH X i   hH

H h  f net hH

X i x W XH
ih
+
 f
 hH
 Activation function : sigmoid
1
f x  
1  e  ax 
Blue a=2
Red a=1
Green a=0.5
4
k
L

Back propagation neural network (BPNN)
◦ Hidden-to-output
 Weight
HY
Y




net
h Whj H h
j
Y j  f net Yj 
Y
j
 Activation function : pure-linear
f x   x
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H h x W HY
hj
+
 Yj
 f

Back propagation neural network (BPNN)
◦ Adjust weights
XH
ih
W
a
WhjHY
1
b
k
E
L
F
2
c
3
d
Output layer
4
Input layer

H
h
e
Hidden layer
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 Yj
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Back propagation neural network (BPNN)
◦ Adjust weights
XH
ih
W
W
P   W P  1  W
XH
ih
XH
ih
XH
ih
E
 
XH
Wih
1
2
E    d h  H h 
2 h
 : 學習速率
d h :目標輸出值
E : 誤差函數
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Support vector machine (SVM)
◦ Classification
◦ Statistics
◦ Hyperplane
optimal separating hyperplane (OSH)
support hyperplane
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Margin
OSH:最佳分割超平面
Support hyperplane:支持超平面
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T.O.V.A
◦ MDD
 Higher omission rates
 Higher mean response times
 More variability
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EMG features
◦ MDD
 Lower EA and RMS values
 Higher MF and MPF values
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T.O.V.A
◦ Compare EMG Comparison by groups during rest
EA(uv)
RMS(uv)
MDF
(PSD)
MPF
(PSD)
Groups
Mean
SD
MDD
22.20
34.84
Control
56.59
53.51
MDD
0.12
0.16
Control
0.28
0.25
MDD
80.40
7.31
Control
73.11
7.77
MDD
84.98
4.19
Control
80.51
4.14
P
0.001
0.000
0.000
0.000
*p<0.05
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T.O.V.A
◦ Compare EMG Comparison by group during TOVA
EA(uv)
RMS(uv)
MDF
(PSD)
MPF
(PSD)
Groups
Mean
SD
MDD
28.46
38.27
Control
65.01
88.40
MDD
0.14
0.18
Control
0.32
0.40
MDD
80.83
6.59
Control
72.79
6.45
MDD
85.22
3.97
Control
80.43
3.31
P
0.013
0.010
0.000
0.000
*p<0.05
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
Accuracy
Accuracy
Rest
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TOVA
Train
Test
Train
Test
BPNN
92.06%
76.67%
87.67%
76.67%
SVM
100%
83.33%
98.33%
83.56%
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MDD becomes more distinguishable when resting
Health controls have wider range of EA and MF
MDD patients have the lower capability on physiological
regulation
Hopefully, the system can be used to detect and to control
the MDD disorder in the future.
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