Extraction and Analysis of EEG signals for the detection of ADHD syndrome – A novel approach

95 Pages·2012·1.89 MB·English

VISVESVARAYA TECHNOLOGICAL UNIVERSITY
“JNANA SANGAMA”, BELAGAVI-590018.
2015-2016
A
Project Report
On
“Extraction and Analysis of EEG signals for the detection of
ADHD syndrome – A novel approach”
Submitted in partial fulfillment for the award of the degree of
BACHELOR OF ENGINEERING
IN
Submitted By
Chandana S [1JB12EC024]
Karthik B Balaraj [1JB12EC041]
Sushmitha Pol [1JB12EC112]
Usha P [1JB10EC115]
UNDER THE GUIDANCE OF
Dr. K. V. Mahendra Prashanth
Professor,Dept.of ECE
Chief Co-ordinator, R&D and PG studies
||Jai Sri Gurudev||
Sri Adichunchanagiri Shikshana Trust ®
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
SJB INSTITUTE OF TECHNOLOGY
B G S HEALTH AND EDUCATION CITY
Kengeri, Bangalore-560060.

ACKNOWLEDGEMENT
We would like to express our profound grateful to His Divine Soul Jagadguru Padmabhushan Sri Sri Sri Dr.
Balagangadharanatha Mahaswamijiand HisHolinessJagadguru Sri Sri SriDr. Nirmalanandanatha Swamiji
for providing us an opportunity tocompleteouracademicsin this esteemed institution.
We would also like to express our profound thanks to Revered Sri Sri Prakashnath Swamiji, Managing Director,
SJB Institute of Technology, for his continuous support in providing amenities to carry out this project in this
admired institution.
We express our gratitude to Dr. Puttaraju, Principal, SJB Institute of Technology, for providing us an excellent
facilities and academic ambience; which have helpedusin satisfactory completion of project work.
We extend our sincere thanks to Dr. K. R. Nataraj, Head of the Department, Electronics & Communication
Engineeringfor providingusan invaluable support throughout the period ofourproject work.
We wish to expressour heartfelt gratitude to our guide Dr. K.V. Mahendra Prashanth, for his valuable guidance,
suggestions and cheerful encouragement during the entire period ofourproject work.
We wish to extend our heartfelt gratitude to Mrs. Vijayalakshmi. K (BMSCE) and her assistants, for their
valuableguidance, suggestions and cheerful encouragement during the entire period of our project work.
We would extend our sincere thanks toSrushti Special Academyfor their kind co-operation and providing valuable
inputs during this project work.
We would also like to express our thanks to theSubjects (Children), Parents and Clarity Medical Systemfor all
their concern and support towards data collection for our project work.
We express our truthful thanks to Dr. K. Mahantesh, Project Coordinator, Department of ECE, for his valuable
support.
Finally,wetake this opportunity to extendourearnest gratitude and respect toourparents, Teaching & Non teaching
staffs of the department, the library staff and allourfriends, who have directly or indirectly supportedusduring the
period ofourproject work.
Regards,
Chandana S
Karthik B Balaraj
SushmithaPol
Usha P
ABSTRACT
Attention Deficit Hyperactivity disorder (ADHD) is a common mental disorder that
begins in childhood and can continue through adolescence and adulthood. EEG is most often
used to diagnose epilepsy, sleep disorders, coma, encephalopathy, and brain death. EEG is used
to be a first-line method of diagnosis for tumors, stroke and other focal brain disorders.
The present project work is mainly designed to predict the probable region of brain that
shows abnormality due to ADHD syndrome. EEG data of seven normal and ADHD subjects
(children) of age group 4-17 years has been collected following a protocol which contains 4
events- Eyes close, Eyes open, Visual Cue and Motor activity. The segmented data is subjected
to extract features like, energy that corresponds to Alpha, Beta, Delta and Theta frequency
ranges; absolute power and amplitude levels at different electrode positions. Single map analysis
and Frequency map analysis is also performed and comparative analysis is done between the
normal and ADHD subjects. Neural network algorithm is implemented to distinguish between
ADHD and normal subjects. Finally, 3-D plotting is done for the ease of visualization and
diagnosis purpose.
This project work draws the conclusion that, posterior region of the brain, covering
electrode positions O1, O2, P3 and P4 could be considered to predict the abnormality during
eyes open and eyes close; Anterior portion of the brain, covering pre-frontal regions and frontal
region of the brain, with electrode positions FP1 and FP2 could be used to predict the
abnormality. It also proves that, neural networks, if well-trained can be used to distinguish
between ADHD and normal subjects, given the pattern of EEG as input.
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LIST OF FIGURES
Sl. No. Figure No. Name of Figure Page No.
1. 1.1 Brain 4
2. 1.2 Electroencephalogram 7
3. 1.3 An EEG setup 7
4. 3.1 Flowchart 17
5. 3.2 International 10-20 electrode system 21
6. 3.3 Braintech traveller portable EEG device 22
7. 3.4 Longitudinal Bipolar montage 26
8. 3.5 Protocol used 28
9. 3.6 Block diagram of FFT algorithm 30
10. 3.7 Frequency Maps 35
11. 3.8 Frequency Tables 35
12. 3.9 Training and verification 37
13. 3.10 Single Layer feed-forward 39
14. 3.11 Multi-Layerfeed-forward 39
15. 3.12 Recurrent Network 40
16. 3.13 The Neural Network Diagram 40
17. 3.14 Performance plot 41
18. 3.15 Error Histogram 42
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19. 3.16 Regression Plot 43
20. 3.17 Confusion Plot 43
21. 4.1 Frequency map of ADHD subject in eyes close event 53
22. 4.2 Frequency map of normal subject in eyes close event 54
23. 4.3 Frequencymap of ADHD subject in eyes open event 54
24. 4.4 Frequencymap of normal subject in eyes open event 55
25. 4.5 Frequencymap of ADHD subject in visual cue event 55
26. 4.6 Frequencymap of normal subject in visual cue event 56
27. 4.7 Frequency map of ADHD subject in motor activity event 56
28. 4.8 Frequency map of normal subject in motor activity event 57
29. 4.9 Performance Plot (Case-1) 58
30. 4.10 Regression Plot (Case-1) 59
31. 4.11 Training State Plot (Case-1) 60
32. 4.12 Confusion Matrix -Train State(Case-1) 61
33. 4.13 Confusion Matrix -Test State(Case-1) 61
34. 4.14 Error Histogram 1 (Case-1) 62
35. 4.15 Error Histogram 2 (Case-1) 62
36. 4.16 Performance Plot (Case-2) 62
37. 4.17 Regression Plot (Case-2) 63
38. 4.18 Training State Plot (Case-2) 63
39. 4.19 Confusion Matrix -Train State(Case-2) 64
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40. 4.20 Confusion Matrix -Test State(Case-2) 64
41. 4.21 Error Histogram 1 (Case-2) 65
42. 4.22 ErrorHistogram 2 (Case-2) 65
LIST OF TABLES
Sl. No. Table No. Name of Table Page No.
1. 1.1 Activation Patterns in Brain 5
2. 1.2 Classification of EEG Signals 9
3. 3.1 Details of ADHD Subjects 20
4. 3.2 Details of Normal Subjects 21
5. 3.3 OperatingEnvironment of Braintech Traveller 23
6. 3.4 General Parameters of Braintech Traveller Device 23
7. 3.5 Channels of Longitudinal Bipolar Montage 26
8. 4.1 Single Map Analysis 44
9. 4.2 Standard Deviation Table for Eyes Close Event 45
10. 4.3 Standard Deviation Table for Eyes Open Event 45
11. 4.4 Amplitude Values for ADHD Subjects 46
12. 4.5 Amplitude Values for Normal Subjects 47
13. 4.6 Standard Deviation Table for Visual Cue 48
14. 4.7 RelativePowerPercentageTable for ADHD Subjects in 48
Eyes CloseEvent
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15. 4.8 RelativePowerPercentageTable forNormal Subjects in 49
Eyes CloseEvent
16. 4.9 RelativePowerPercentageTable for ADHD Subjects in 49
Eyes Open Event
17. 4.10 RelativePowerPercentageTable forNormal Subjects in 49
Eyes Open Event
18. 4.11 RelativePowerPercentageTable for ADHD Subjects in 50
Visual Cue Event (Animal)
19. 4.12 RelativePowerPercentageTable for ADHD Subjects in 50
Visual Cue Event (Color)
20. 4.13 RelativePowerPercentageTable for ADHD Subjects in 51
Visual Cue Event (Number)
21. 4.14 RelativePowerPercentageTable forNormal Subjects in 51
Visual Cue Event (Animal)
22. 4.15 RelativePowerPercentageTable forNormal Subjects in 51
Visual Cue Event (Color)
23. 4.16 RelativePowerPercentageTable forNormal Subjects in 52
Visual Cue Event (Number)
24. 4.17 RelativePowerPercentageTable forADHDSubjects in 52
Motor Activity
25. 4.18 RelativePowerPercentageTable forNormal Subjects in 53
Motor Activity
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TABLE OF CONTENTS
Abstract iii
List of Figures iv
List of Tables vi
CHAPTER 1: INTRODUCTION 1
1.1 Attention Deficit HyperactivityDisorder (ADHD) 1
1.1.1 Types of ADHD 1
1.1.2 Symptoms of ADHD 2
1.1.3 Causes for ADHD 2
1.1.4 Brain Functioning in ADHD 3
1.1.4.1 Structural Abnormality 3
1.1.4.2 Functional Abnormality 4
1.1.4.3 Functional circuits involved in the pathophysiology of ADHD 4
as identified in a review of the neurobiology of ADHD
1.1.4.4 Electrical activity 5
1.1.4.5 Chemical Imbalance 6
1.2 Electroencephalography 6
1.2.1 EEG Signal Classification 8
1.2.2 Advantages 8
1.2.3 Disadvantages 11
1.2.4 EEG vs. fMRI, fNIRS and PET 11
1.2.5 EEG vs.MEG 12
CHAPTER 2: LITERATURE SURVEY 13
2.1 Motivation 15
2.2 Objectives 16
2.3 Challenges 16
CHAPTER 3: METHODOLOGY 17
3.1 Subjects 18
3.1.1 Questionnaire 18
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3.1.2 Subject Details 20
3.2 EEG Data Acquisition 21
3.2.1 Braintech Traveler Portable EEG Device 22
3.2.1.1 Configurations 22
3.2.1.2 General Parameters 23
3.2.1.3 MoreFeatures 24
3.2.2 Electrode Positions 25
3.2.3 Protocol 27
3.3 Data Segmentation 28
3.4 Feature Extraction 30
3.4.1 Energy Calculation 30
3.4.2 Statistical Parameters Calculation 31
3.5 Analysis 33
3.5.1 Single Map Analysis 34
3.5.2 FrequencyMap Analysis 34
3.5.3 Frequency Tables 35
3.6 Neural Network Algorithm and its Importance 36
3.6.1 Why Neural Networks 37
3.6.2 Feed forward back propagation 37
3.6.3 Network architectures 38
3.6.3.1 Single Layer Feed-forward 38
3.6.3.2 Multi-layer feed-forward 39
3.6.3.3 Recurrent network 40
3.6.4 The Neuron 40
3.6.5 Performance plot and Training state plot 41
3.6.6 Error Histogram 42
3.6.7 Regression Plot 42
3.6.8 Confusion Plot 43
CHAPTER 4: RESULTS 44
4.1 Single Map Analysis 44
4.2 Power Analysis 48
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