| Description |
1 online resource |
| Bibliography |
Includes bibliographical references and index. |
| Note |
Online resource; title from PDF title page (ScienceDirect, viewed February 21, 2019). |
| Contents |
1.5. Swarm Intelligence1.6. Tools for Feature Extraction; 1.7. Contributions; 1.8. Summary and Structure of Book; Chapter 2: Literature Survey; 2.1. EEG Signal Analysis Methods; 2.2. Preprocessing of EEG Signal; 2.3. Tasks of EEG Signal; 2.4. Classical vs Machine Learning Methods for EEG Classification; 2.5. Machine Learning Methods for Epilepsy Classification; 2.6. Summary; Chapter 3: Empirical Study on the Performance of the Classifiers in EEG Classification; 3.1. Multilayer Perceptron Neural Network; 3.1.1. MLPNN With Back-Propagation; 3.1.2. MLPNN With Resilient Propagation |
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3.1.3. MLPNN With Manhattan Update Rule3.2. Radial Basis Function Neural Network; 3.3. Probabilistic Neural Network; 3.4. Recurrent Neural Network; 3.5. Support Vector Machines; 3.6. Experimental Study; 3.6.1. Datasets and Environment; 3.6.2. Parameters; 3.6.3. Results and Analysis; 3.7. Summary; Chapter 4: EEG Signal Classification Using RBF Neural Network Trained With Improved PSO Algorithm for Epilepsy Identification; 4.1. Related Work; 4.2. Radial Basis Function Neural Network; 4.2.1. RBFNN Architecture; 4.2.2. RBFNN Training Algorithm; 4.3. Particle Swarm Optimization |
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4.3.1. Architecture4.3.2. Algorithm; 4.4. RBFNN With Improved PSO Algorithm; 4.4.1. Architecture of Proposed Model; 4.4.2. Algorithm for Proposed Model; 4.5. Experimental Study; 4.5.1. Dataset Preparation and Environment; 4.5.2. Parameters; 4.5.3. Results and Analysis; 4.6. Summary; Chapter 5: ABC Optimized RBFNN for Classification of EEG Signal for Epileptic Seizure Identification; 5.1. Related Work; 5.2. Artificial Bee Colony Algorithm; 5.2.1. Architecture; 5.2.2. Algorithm; 5.3. RBFNN With Improved ABC Algorithm; 5.3.1. Architecture of the Proposed Model |
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5.3.2. Algorithm for the Proposed Model5.4. Experimental Study; 5.4.1. Dataset Preparation and Environment; 5.4.2. Parameters; 5.4.3. Result and Analysis; 5.4.4. Performance Comparison Between Modified PSO and Modified ABC Algorithm; 5.5. Summary; Chapter 6: Conclusion and Future Research; 6.1. Findings and Constraints; 6.2. Future Research Work; References; Index; Back Cover |
| Subject |
Epilepsy -- Diagnosis.
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Electroencephalography.
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Electrophysiological aspects of epilepsy.
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HEALTH & FITNESS -- Diseases -- General.
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MEDICAL -- Clinical Medicine.
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MEDICAL -- Diseases.
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MEDICAL -- Evidence-Based Medicine.
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MEDICAL -- Internal Medicine.
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Electroencephalography. (OCoLC)fst00906445
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Electrophysiological aspects of epilepsy. (OCoLC)fst01921037
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Epilepsy -- Diagnosis.
(OCoLC)fst00914189
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| Added Author |
Dehuri, Satchidananda, author.
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Jagadev, Alok Kumar, author.
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Mishra, Shruti, author.
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| Other Form: |
Print version: Satapathy, Sandeep Kumar. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. San Diego : Elsevier Science & Technology, ©2019 9780128174265 |
| ISBN |
9780128174272 (electronic book) |
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9780128174265 (print) |
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0128174277 |
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