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Epileptic seizures result from a sudden electrical disturbance to
the brain. Approximately one in every 100 persons will experience a
seizure at some time in their life. In this work, we propose a
genetic algorithm, SVM based fuzzy knowledge integration framework
that is used for classification of risk level of epilepsy in
diabetic patients from Electroencephalogram (EEG) signals. A
statistical analysis of the EEG signal to indicate the onset of
epilepsy based on chi square tests and control limits. Ten known
diabetic patients with raw EEG recording are studied. Chapter 1
introduces the features of EEG signals and focus of the research.
Chapter 2 discusses about Statistical analysis and quantification
of Diabetic epilepsy risk through Chi-square tests. Chapter 3
reviews the fundamentals of fuzzy systems. Chapter 4 enumerates the
Genetic algorithms for optimization of epilepsy risk levels. SVM
techniques as a post classifier for epilepsy detection are
discussed in Chapter 5. Results are discussed in Chapter 6. Chapter
7 brings out the conclusion. Chapter 8 shows the Future scope. This
monograph is useful for all Engineering undergraduate, graduates
students and practicing engineers.
The EEG signals are highly subjective and the information about the
various states may appear at random in the time scale. For example,
a time series may be obtained by recording at regular time
intervals the mean electrical activity of a portion of the
mammalian brain. More specifically, by using a time series one can
determine the possibility of constructing an attractor and thereby
establishing the deterministic character of dynamic underlying
system. Such methods from the non linear dynamical theory can be
dragged for better perception of EEG signals. The complexity of
drowsiness estimation and characterizing the EEG signals can be
brought under some chaotic optimization techniques. Chapter1
introduces Chaos, Non linear dynamics and focus of the research.
Chapter 2 discusses the literature survey of correlation dimension
estimation. Chapter 3 and Chapter 4 enumerate the review of LAB
view and Mat lab software for the book. Results are discussed in
Chapter 5. Chapter 6 brings out the conclusion of this work. Future
scope of this work is solemnized in chapter 7. This monograph is
useful for all Engineering undergraduate, graduates students and
practicing engineers.
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