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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R. Harikumar ha conseguito il dottorato di ricerca in Ingegneria I & C presso l'Università Anna di Chennai nell'aprile 2009. Ha 25 anni di esperienza nell'insegnamento a livello universitario. Attualmente è professore presso il Dipartimento di ECE, Bannari Amman Institute of Technology, Sathyamangalam. Le sue aree di interesse sono i segnali biologici e l'elaborazione delle immagini, il soft computing e l'ingegneria delle comunicazioni.
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Taschenbuch. Zustand: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 116 pp. Englisch. Artikel-Nr. 9783659133244
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Taschenbuch. Zustand: Neu. Fuzzy Genetic Algorithms, SVM Methods for Epilepsy Classification | Fuzzy Genetic Algorithms, SVM and Statistical Analysis in Classification of Diabetic Epilepsy Risk Level from EEG Signal | Harikumar Rajaguru (u. a.) | Taschenbuch | 116 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659133244 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Artikel-Nr. 106446828
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