EEG Signal Analysis and Classification: Techniques and Applications

257 Pages·2016·7.58 MB·English

by  Siuly Siuly

Health Information Science
Siuly Siuly
Yan Li
Yanchun Zhang
EEG Signal
Analysis and
Classification
Techniques and Applications
Health Information Science
Series editor
Yanchun Zhang, Victoria University, Melbourne, Victoria, Australia
Editorial Board
Riccardo Bellazzi, University of Pavia, Italy
Leonard Goldschmidt, Stanford University Medical School, USA
Frank Hsu, Fordham University, USA
Guangyan Huang, Victoria University, Australia
Frank Klawonn, Helmholtz Centre for Infection Research, Germany
Jiming Liu, Hong Kong Baptist University, Hong Kong
Zhijun Liu, Hebei University of Engineering, China
Gang Luo, University of Utah, USA
Jianhua Ma, Hosei University, Japan
Vincent Tseng, National Cheng Kung University, Taiwan
Dana Zhang, Google, USA
Fengfeng Zhou, Shenzhen Institutes of Advanced Technology, Chinese Academy
of Sciences, China
With the development of database systems and networking technologies, Hospital
Information Management Systems (HIMS) and web-based clinical or medical
systems (such as the Medical Director, a generic GP clinical system) are widely
used in health and clinical practices. Healthcare and medical service are more
data-intensive and evidence-based since electronic health records are now used to
track individuals’ and communities’ health information. These highlights substan-
tially motivate and advance the emergence and the progress of health informatics
research and practice. Health Informatics continues to gain interest from both
academia and health industries. The significant initiatives of using information,
knowledge and communication technologies in health industries ensures patient
safety, improve population health and facilitate the delivery of government
healthcare services. Books in the series will reflect technology’s cross-disciplinary
research in IT and health/medical science to assist in disease diagnoses, treatment,
prediction and monitoring through the modeling, design, development, visualiza-
tion,integrationandmanagementofhealthrelatedinformation.Thesetechnologies
include information systems, web technologies, data mining, image processing,
userinteractionandinterfaces,sensorsandwirelessnetworking,andareapplicable
toawiderangeofhealth-relatedinformationsuchasmedicaldata,biomedicaldata,
bioinformatics data, and public health data.
More information about this series at http://www.springer.com/series/11944
Siuly Siuly Yan Li Yanchun Zhang
(cid:129) (cid:129)
EEG Signal Analysis
fi
and Classi cation
Techniques and Applications
123
Siuly Siuly YanchunZhang
Centrefor Applied Informatics, Collegeof Centrefor Applied Informatics, Collegeof
EngineeringandScience EngineeringandScience
Victoria University Victoria University
Melbourne, Victoria Melbourne, Victoria
Australia Australia
Yan Li and
Schoolof Agricultural, Computational and
Environmental Sciences, Faculty of Schoolof Computer Science
Health,Engineering andSciences FudanUniversity
University of SouthernQueensland Shanghai
Toowoomba, Queensland China
Australia
ISSN 2366-0988 ISSN 2366-0996 (electronic)
HealthInformation Science
ISBN978-3-319-47652-0 ISBN978-3-319-47653-7 (eBook)
DOI 10.1007/978-3-319-47653-7
LibraryofCongressControlNumber:2016959570
©SpringerInternationalPublishingAG2016
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To people who are suffering from
neurological diseases and disorders
Preface
Electroencephalography(EEG)hasplayedaprominentroleinbrainstudies,mental
and brain diseases’ and disorders’ diagnosis, and treatments in medical fields. The
examination of EEG signals has been recognized as the most preponderant
approach to the problem of extracting knowledge of brain dynamics. At present,
EEG recordings are widely used for epilepsy diagnosis and for brain computer
interfaces(BCIs).ThemainuseofanEEGistodetectandinvestigateepilepsythat
causes seizures. In BCI systems, EEG signals help to restore sensory and motor
functions in patients who have severe motor disabilities. Generally, the vast
amountsofmulti-channelEEGsignalsarevisuallyanalysedbyanexperttoidentify
and understand abnormalities within the brain and how they propagate.
The visual inspection approach for such huge EEG data is not a satisfactory
procedure for accurate and reliable diagnosis and interpretation as this process is
time-consuming,burdensome,reliantonexpensivehumanresources,andsubjectto
errorandbias.Anextensiveamountofresearchisrequiredforautomaticdiagnosis
ofepilepticseizuresandalsofortheautomaticidentificationofmentalstatestohelp
motor disabled people through BCI systems. Hence, inthis monograph, we aim to
develop advanced methods for the analysis and classification of epileptic EEG
signals and also for the identification of mental states in BCI applications.
A number of edited books have been published in these two areas (but never
have they been presented together in one book) and those books present common
signal processing techniques such as, wavelet transformation, Fourier transforma-
tionforEEGdataanalysis.Thisbook,however,presentssomedifferentEEGsignal
analysis approaches; combining statistical techniques (e.g. random sampling,
optimum allocation, etc.) and machine learning methods. In this book, the authors
present their methods that provide better performance compared to the existing
methods.
The book consists offour parts with 13 chapters. Part I provides a basic over-
view of EEG signals including concept, generation procedure, characteristics,
nature and abnormal patterns. This part also provides a discussion of the different
applicationsofEEGsignalsforthediagnosisofbraindiseasesandabnormalities.In
addition,weprovidetheaimsofthisbook,descriptionofanalyzeddatasetsusedin
vii
viii Preface
the research, performance evaluation measures and a short review of commonly
used methods in EEG signal classification in Part I. Part II presents our developed
techniques and models for the detection of epileptic seizures through EEG signal
processing. Implementation of these proposed methods in real-time databases will
also be highlighted. In Part III, we introduce the methods for identifying mental
states from EEG data designed for BCI systems and their applications in several
benchmark datasets. We also report the experimental procedure with the results of
each methodology. Finally, we provide an overall discussion on EEG signal
analysis and classification in Part IV. This part gives a summary discussion on the
developed methods, future directions in the EEG signal analysis area and conclu-
sions with suggestions for future research.
Melbourne, Australia Siuly Siuly
Toowoomba, Australia Yan Li
Melbourne, Australia and Shanghai, China Yanchun Zhang
Contents
Part I Introduction
1 Electroencephalogram (EEG) and Its Background. .... ..... .... 3
1.1 What Is EEG?. .... ..... .... .... .... .... .... ..... .... 3
1.2 Generation Organism of EEG Signals in the Brain.. ..... .... 7
1.3 Characteristics and Nature of EEG Signals.... .... ..... .... 11
1.4 Abnormal EEG Signal Patterns. .... .... .... .... ..... .... 14
References. .... .... .... ..... .... .... .... .... .... ..... .... 19
2 Significance of EEG Signals in Medical and Health Research. .... 23
2.1 EEG in Epilepsy Diagnosis ... .... .... .... .... ..... .... 24
2.2 EEG in Dementia Diagnosis... .... .... .... .... ..... .... 26
2.3 EEG in Brain Tumour Diagnosis ... .... .... .... ..... .... 27
2.4 EEG in Stroke Diagnosis . .... .... .... .... .... ..... .... 28
2.5 EEG in Autism Diagnosis. .... .... .... .... .... ..... .... 28
2.6 EEG in Sleep Disorder Diagnosis... .... .... .... ..... .... 29
2.7 EEG in Alcoholism Diagnosis . .... .... .... .... ..... .... 30
2.8 EEG in Anaesthesia Monitoring.... .... .... .... ..... .... 30
2.9 EEG in Coma and Brain Death .... .... .... .... ..... .... 31
2.10 EEG in Brain–Computer Interfaces (BCIs).... .... ..... .... 32
2.11 Significance of EEG Signal Analysis and Classification... .... 34
2.12 Concept of EEG Signal Classification.... .... .... ..... .... 35
2.13 Computer-Aided EEG Diagnosis ... .... .... .... ..... .... 38
References. .... .... .... ..... .... .... .... .... .... ..... .... 39
3 Objectives and Structures of the Book... .... .... .... ..... .... 43
3.1 Objectives.... .... ..... .... .... .... .... .... ..... .... 43
3.2 Structure of the Book.... .... .... .... .... .... ..... .... 44
3.3 Materials. .... .... ..... .... .... .... .... .... ..... .... 46
3.3.1 Analyzed Data... .... .... .... .... .... ..... .... 46
3.3.2 Performance Evaluation Parameters... .... ..... .... 50
ix
x Contents
3.4 Commonly Used Methods for EEG Signal Classification.. .... 52
3.4.1 Methods for Epilepsy Diagnosis . .... .... ..... .... 52
3.4.2 Methods for Mental State Recognition in BCIs... .... 54
References. .... .... .... ..... .... .... .... .... .... ..... .... 56
PartII TechniquesfortheDiagnosisofEpilepticSeizuresfromEEG
Signals
4 Random Sampling in the Detection of Epileptic EEG Signals. .... 65
4.1 Why Random Sampling in Epileptic EEG Signal
Processing?... .... ..... .... .... .... .... .... ..... .... 65
4.2 SimpleRandomSamplingBasedLeastSquareSupportVector
Machine . .... .... ..... .... .... .... .... .... ..... .... 67
4.2.1 Random Sample and Sub-sample Selection Using
SRS Technique .. .... .... .... .... .... ..... .... 68
4.2.2 Feature Extraction from Different Sub-samples... .... 69
4.2.3 Least Square Support Vector Machine (LS-SVM) for
Classification.... .... .... .... .... .... ..... .... 70
4.3 Experimental Results and Discussions ... .... .... ..... .... 72
4.3.1 Results for Epileptic EEG Datasets... .... ..... .... 73
4.3.2 Results for the Mental Imagery Tasks EEG Dataset ... 78
4.3.3 Results for the Two-Class Synthetic Data.. ..... .... 79
4.4 Conclusions .. .... ..... .... .... .... .... .... ..... .... 81
References. .... .... .... ..... .... .... .... .... .... ..... .... 81
5 A Novel Clustering Technique for the Detection of Epileptic
Seizures .. .... .... .... ..... .... .... .... .... .... ..... .... 83
5.1 Motivation ... .... ..... .... .... .... .... .... ..... .... 84
5.2 Clustering Technique Based Scheme .... .... .... ..... .... 84
5.2.1 Clustering Technique (CT) for Feature Extraction. .... 85
5.3 Implementation of the Proposed CT-LS-SVM Algorithm.. .... 87
5.4 Experimental Results and Discussions ... .... .... ..... .... 89
5.4.1 Classification Results for the Epileptic EEG Data. .... 89
5.4.2 Classification Results for the Motor Imagery EEG’
Data.. .... ..... .... .... .... .... .... ..... .... 92
5.5 Conclusions .. .... ..... .... .... .... .... .... ..... .... 96
References. .... .... .... ..... .... .... .... .... .... ..... .... 96
6 A Statistical Framework for Classifying Epileptic Seizure from
Multi-category EEG Signals... .... .... .... .... .... ..... .... 99
6.1 Significance of the OA Scheme in the EEG Signals Analysis
and Classification .. ..... .... .... .... .... .... ..... .... 99
6.2 Optimum Allocation-Based Framework .. .... .... ..... .... 100
6.2.1 Sample Size Determination. .... .... .... ..... .... 101
6.2.2 Epoch Determination.. .... .... .... .... ..... .... 102
6.2.3 Optimum Allocation .. .... .... .... .... ..... .... 103