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.
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