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

 
9783659133244: 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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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 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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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Fuzzy Genetic Algorithms, SVM and Statistical Analysis in Classification of Diabetic Epilepsy Risk Level from EEG Signal | 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. | Format: Paperback | Language/Sprache: english | 116 pp. Seller Inventory # K9783659133244

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Book Description LAP Lambert Academic Publishing Mai 2012, 2012. 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. 116 pp. Englisch. Seller Inventory # 9783659133244

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Book Description LAP Lambert Academic Publishing Mai 2012, 2012. 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. 116 pp. Englisch. Seller Inventory # 9783659133244

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Book Description LAP Lambert Academic Publishing. Paperback. Condition: New. 116 pages. Dimensions: 8.7in. x 5.9in. x 0.3in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783659133244

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Vinoth Kumar Bojan, Harikumar Rajaguru, Vijayakumar Thangavel
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Book Description LAP Lambert Academic Publishing, Germany, 2012. Paperback. Condition: New. Aufl.. Language: English. Brand new Book. 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 # AAV9783659133244

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