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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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Introduction
Purpose
Methods and Materials
Results
Conclusion and Discussion
Future work
References
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Major Depression Disorder (MDD)
◦ Mental disorder with symptoms
◦ Life-prevalence rate is 25%
◦ The annual economic consequences of depression are huge
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Clinical symptoms
◦ Emotional
◦ Ideational
◦ Somatic symptoms
Major Depression Disorder:重鬱症
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Misdiagnose
◦ Physicians tend to under-recognize
◦ Patients tend to lie
◦ Objective measurement on depression
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Physiological mechanisms
◦ Electroencephalography (EEG)
◦ Electromyogram (EMG)
◦ Electrocardiogram (ECG)
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Electromyogram (EMG)
◦ Depression patients often have symptoms of stiffness and
soreness necks
◦ Carney’s research at 1981
◦ Splenius capitis muscle
Splenius capitis muscle
Splenius capitis muscle : 頭夾肌肉
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The aim of this study is to investigate the splenius
capitis muscle with multiple EMG features for MDD
classification
◦ Multiple EMG features
◦ An artificial neural network (ANN)
◦ Support vector machine (SVM)
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Design Consideration
people
males
females
years
MDD
45
19
26
41.96±11.73
Control
45
19
26
42.42±11.63
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The MDD and control groups are identified by
◦ Hamilton Depression Rating Scale (HAMD)
 HAMD-17
◦ Beck Depression Inventory Scale (BDI)
 21-question multiple-choice self-report inventory
◦ Beck Anxiety Inventory Scale (BAI)
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Two gender-age matched groups
◦ Training: 30 subjects
◦ Testing: 60 subjects
MDD
Control
45
45
30
60
Training
Testing
Unit : number of subjects
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Data collection
◦ Time: 9:00 am~12:00 pm
◦ Test timetable
 Rest : 2 min
 Test of Variables of Attention (T.O.V.A) : 22 min
 The EMG of splenius capitis muscle : 24 min
24min/once test
2min
Rest
22min
Test of Variables of Attention (T.O.V.A)
EMG: 30 to 1000Hz using MP35
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EMG features analysis
◦ Time domain
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EA 
t T
EMG (t )dt
t
RMS 
T
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t T
t
EMG 2 (t )dt
T
Electromyographic activity (EA) : 肌電訊號活躍度
Root mean square (RMS) : 均方根
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EMG features analysis
◦ Frequency domain
MDF
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0
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PSD( f )df   PSD( f )df   PSD( f )df
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MDF
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MPF 
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f  PSD( f )df
0
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0
PSD( f )df
Median frequency (MDF): 中位頻率
Mean power frequency (MPF): 平均功率頻率
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