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 - Softcover
Synopsis
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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About the Author
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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Taschenbuch. Condition: 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. Seller Inventory # 9783659133244
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Fuzzy Genetic Algorithms, SVM Methods for Epilepsy Classification
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Condition: 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. Seller Inventory # 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. Condition: 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. Seller Inventory # 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
ISBN 10: 3659133248
ISBN 13: 9783659133244
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Taschenbuch. Condition: 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. Seller Inventory # 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. Condition: 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. Seller Inventory # 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
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ISBN 10: 3659133248
ISBN 13: 9783659133244
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