Table Of ContentAUTOMATED
EEG BASED DIAGNOSIS
OF
NEUROLOGICAL
DISORDERS
Inventing the Future of Neurology
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AUTOMATED
EEG-BASED DIAGNOSIS
OF
NEUROLOGICAL
DISORDERS
Inventing the Future of Neurology
Hojjat Adeli
Samanwoy Ghosh-Dastidar
In corroboration with Nahid Dadmehr, M.D.
CRC Press is an imprint of the
Taylor & Francis Group, an informa business
Boca Raton London New York
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Dedicated to
Dr. Nahid, Dr. Anahita, Dr. Amir, Mona, and Cyrus Dean
Adeli
Kanan, Somashree, Manisha, and Indrajit
Ghosh Dastidar
Preface
Based on the authors’ ground-breaking research, the book presents an ideol-
ogy, a novel multi-paradigm methodology, and advanced computational mod-
els for automated EEG-based diagnosis of neurological disorders that the au-
thors believe will be the wave of the future and an important tool in the prac-
tice of neurology. It is based on adroit integration of three different computing
technologies and problem solving paradigms: neural networks, wavelets, and
chaos theory. The book also includes three introductory chapters in order to
introduce the readers to these three different computing paradigms.
Epilepsy, the primary application focus of the book, is a common disor-
der affecting approximately 1% of the population in the United States and
is commonly accompanied by intermittent abnormal firing of neurons in the
brain leading to recurrent and spontaneous seizures (with no apparent ex-
ternal cause or trigger). At present, epileptic seizure detection and epilepsy
diagnosis are performed primarily based on visual examinations of electroen-
cephalograms (EEGs) by highly trained neurologists. While many attempts
have been reported in the literature none has been accurate enough to per-
form better than practicing neurologists/epileptologists. Effective algorithms
for automatic seizure detection and prediction can have a far-reaching impact
on diagnosis and treatment of epilepsy.
In this book, the clinical epilepsy and seizure detection problem is mod-
eled as a three-group classification problem. The three subject groups are: a)
healthy subjects (normal EEG), b) epileptic subjects during a seizure-free in-
terval (interictal EEG), and c) epileptic subjects during a seizure (ictal EEG).
viii
Epilepsy diagnosis is modeled as the classification of normal EEGs and inter-
ictal EEGs. Seizure detection is modeled as the classification of interictal and
ictal EEGs.
The approach presented in this book challenges the assumption that the
EEG represents the dynamics of the entire brain as a unified system and needs
to be treated as a whole. On the contrary, an EEG is a signal that represents
the effect of the superimposition of diverse processes in the brain. There is
no good reason why the entire EEG should be more representative of brain
dynamics than the individual frequency sub-bands. In fact, the sub-bands
may yield more accurate information about constituent neuronal activities
underlying the EEG and, consequently, certain changes in the EEGs that are
not evident in the original full-spectrum EEG may be amplified when each
sub-band is analyzed separately. This is a fundamental premise of the authors’
approach.
After extensive research and discovery of mathematical markers, the au-
thors present a methodology for epilepsy diagnosis and seizure detection with a
high accuracy of 96%. The technology presented in the book outperforms prac-
ticing neurologists/epileptologists. It has the potential to impact and trans-
form part of the neurology practice in a significant way.
The book also includes some preliminary results toward EEG-based di-
agnosis of Alzheimer’s disease (AD) which is admittedly in its infancy. But
the preliminary findings presented in the book provide the potential for a
major breakthrough for diagnosis of AD. The methodology presented in the
book is general and can be adapted and applied for diagnosis of other brain
disorders. The senior author and his research associates are currently extend-
ing the work to automated EEG-based diagnosis of AD and other neurological
disorders such Attention-Deficit/Hyperactivity Disorder (ADHD) and autism.
A second contribution of the book is presenting and advancing Spiking
ix
Neural Networks as the seminal foundation of a more realistic and plausi-
ble third generation neural network. It is hoped the fundamental research in
this area of neuronal modeling will advance in the coming years resulting in
more powerful computational neural network models not only for diagnosis of
neurological disorders but also many other complex and intractable dynamic
pattern recognition and prediction phenomena.
Hojjat Adeli
Samanwoy Ghosh-Dastidar
Acknowledgments
Since the authors’ overarching goal in this work was to make a significant im-
pact on the future practice of neurology the neurological aspects of the models
presented in the book were reviewed and corroborated by a board-certified
neurologist, Nahid Dadmehr, M.D., in practice for nearly two decades. Her
contribution to this work and to the future of neurology practice is gratefully
acknowledged. We are also grateful to Dr. Dennis Duke of Florida State Uni-
versity and Dr. Kerry Coburn of Mercer University for providing EEG data
on AD patients used in Chapter 12.
Parts of the work presented in this book were published by the authors
in several research journals: IEEE Transactions on Biomedical Engineering
(published by IEEE), Neural Networks (published by Elsevier), Neuroscience
Letters (published by Elsevier), Journal of Alzheimer’s Disease (published by
IOS Press), Clinical EEG and Neuroscience (published by EEG and Clinical
Neuroscience Society), Integrated Computer-Aided Engineering (published by
IOS Press), and International Journal of Neural Systems (published by World
Scientific), as noted in the list of references and cited throughout the book.
Chapter 6 is based on a journal article by the senior author, his former re-
search associate, Ziqin Zhou, and Dr. Nahid Dadmehr, and is reproduced by
permission of Elsevier, the publisher of the journal.
About the Authors
Hojjat Adeli received his Ph.D. from Stanford University in 1976 at the
age of 26 after graduating from the University of Tehran in 1973 with the
highest rank among the graduates of the entire College of Engineering. He
is currently Professor of Civil and Environmental Engineering and Geodetic
Science and the holder of the Abba G. Lichtenstein Professorship at The Ohio
State University. He is also Professor of Aerospace Engineering, Biomedical
Engineering, Biomedical Informatics, Electrical and Computer Engineering,
Neurological Surgery and Neuroscience by courtesy. He has authored over 450
research and scientific publications in various fields of computer science, engi-
neering, applied mathematics, and medicine including 240 journal articles and
fourteen books. His wide-ranging research has been published in 74 different
journals. He has also edited thirteen books. He is the Founder and Editor-
in-Chief of the international research journals Computer-Aided Civil and In-
frastructure Engineering, in publication since 1986, and Integrated Computer-
Aided Engineering, in publication since 1993, and Editor-in-Chief of Interna-
tional Journal Neural Systems. He is the quadruple winner of The Ohio State
University College of Engineering Lumley Outstanding Research Award. In
1998 he received the Distinguished Scholar Award, The Ohio State Univer-
sity’s highest research award “in recognition of extraordinary accomplishment
in research and scholarship”. In 2005, he was elected Honorary/Distinguished
Member, American Society of Civil Engineers:“for wide-ranging, exceptional,
and pioneering contributions to computing in civil engineering disciplines and
extraordinary leadership in advancing the use of computing and information
