EEG brain signal classification for epileptic seizure disorder detection
| Main Author: | Satapathy, Sandeep Kumar, |
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| Other Authors: | Dehuri, Satchidananda,, Jagadev, Alok Kumar,, Mishra, Shruti,, ScienceDirect (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
London, United Kingdom :
Academic Press, an imprint of Elsevier,
2019.
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| Physical Description: |
1 online resource. |
| Subjects: |
| Item Description: |
Includes bibliographical references and index. 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. 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. 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. 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. |
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| Physical Description: |
1 online resource. |
| Bibliography: |
Includes bibliographical references and index. |
| ISBN: |
0128174277 9780128174272 |