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 - Brossura
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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.
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L'autore
R.Harikumar was awarded Ph.D in I &C Engg from Anna university Chennai in April 2009. He has 22 years of teaching experience at college level. Currently he is Professor ECE Department at Bannari Amman Institute of Technology, Sathyamangalam.His area of interest is Bio signal Processing, Soft computing, VLSI Design and Communication Engineering.
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Fuzzy Genetic Algorithms, SVM Methods for Epilepsy Classification
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LAP LAMBERT Academic Publishing Mai 2012, 2012
ISBN 10: 3659133248
ISBN 13: 9783659133244
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 116 pp. Englisch. Codice articolo 9783659133244
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Fuzzy Genetic Algorithms, SVM Methods for Epilepsy Classification
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rajaguru HarikumarR.Harikumar was awarded Ph.D in I &C Engg from Anna university Chennai in April 2009. He has 22 years of teaching experience at college level. Currently he is Professor ECE Department at Bannari Amman Institute of T. Codice articolo 5133826
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Fuzzy Genetic Algorithms, SVM Methods for Epilepsy Classification
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LAP LAMBERT Academic Publishing Mai 2012, 2012
ISBN 10: 3659133248
ISBN 13: 9783659133244
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch. Codice articolo 9783659133244
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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
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LAP LAMBERT Academic Publishing, 2012
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ISBN 13: 9783659133244
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9783659133244
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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
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Taschenbuch. Condizione: 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: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 106446828
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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
Editore:
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659133248
ISBN 13: 9783659133244
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