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EEG brain signal classification for ...

Satapathy, Sandeep Kumar,

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EEG brain signal classification for epileptic seizure disorder detection

Record Type:	Electronic resources : Monograph/item
Title/Author:	EEG brain signal classification for epileptic seizure disorder detection/ Sandeep Kumar Satapathy, Satchidananda Dehuri, Alok KumarJagadev, Shruti Mishra.
Author:	Satapathy, Sandeep Kumar,
other author:	Dehuri, Satchidananda,
Published:	London, United Kingdom :Academic Press, an imprint of Elsevier, : 2019.,
Description:	1 online resource.
[NT 15003449]:	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
[NT 15003449]:	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
[NT 15003449]:	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
[NT 15003449]:	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. -
Online resource:	https://www.sciencedirect.com/science/book/9780128174265
ISBN:	9780128174272 (electronic bk.)

EEG brain signal classification for epileptic seizure disorder detection
Satapathy, Sandeep Kumar,

EEG brain signal classification for epileptic seizure disorder detection[electronic resource] /Sandeep Kumar Satapathy, Satchidananda Dehuri, Alok KumarJagadev, Shruti Mishra. - London, United Kingdom :Academic Press, an imprint of Elsevier,2019. - 1 online resource.

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

ISBN: 9780128174272 (electronic bk.)Subjects--Topical Terms:

834429
Epilepsy
--Diagnosis.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: RC373

Dewey Class. No.: 616.85/307547

EEG brain signal classification for epileptic seizure disorder detection
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020 $a 9780128174272 (electronic bk.)
020 $a 0128174277 (electronic bk.)
020 $a 9780128174265 (pbk.)
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041 0 $a eng
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100 1 $a Satapathy, Sandeep Kumar, $e author. $3 3462549
245 1 0 $a EEG brain signal classification for epileptic seizure disorder detection $h [electronic resource] / $c Sandeep Kumar Satapathy, Satchidananda Dehuri, Alok KumarJagadev, Shruti Mishra.
260 $a London, United Kingdom : $b Academic Press, an imprint of Elsevier, $c 2019.
300 $a 1 online resource.
504 $a Includes bibliographical references and index.
505 8 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
588 0 $a Online resource; title from PDF title page (ScienceDirect, viewed February 21, 2019).
650 0 $a Epilepsy $x Diagnosis. $3 834429
650 0 $a Electroencephalography. $3 543293
650 0 $a Electrophysiological aspects of epilepsy. $3 3462553
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Dehuri, Satchidananda, $e author. $3 3462550
700 1 $a Jagadev, Alok Kumar, $e author. $3 3462551
700 1 $a Mishra, Shruti, $e author. $3 3462552
856 4 0 $u https://www.sciencedirect.com/science/book/9780128174265