ISBN 978-981-10-6529-3 (eBook) ... 1.6 Key Problems in EEG Classification Methods ................ 8 ... 2 Analysis of Electroencephalogram (EEG) Using ANN .
SpringerBriefs in Applied Sciences and Technology Forensic and Medical Bioinformatics
Series editors Amit Kumar, Hyderabad, India Allam Appa Rao, Hyderabad, India
More information about this series at http://www.springer.com/series/11910
Sasikumar Gurumoorthy Naresh Babu Muppalaneni Xiao-Zhi Gao
Computational Intelligence Techniques in Diagnosis of Brain Diseases
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Sasikumar Gurumoorthy Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India
Xiao-Zhi Gao Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Lappeenranta Finland
Naresh Babu Muppalaneni Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India
ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISSN 2196-8845 ISSN 2196-8853 (electronic) SpringerBriefs in Forensic and Medical Bioinformatics ISBN 978-981-10-6528-6 ISBN 978-981-10-6529-3 (eBook) DOI 10.1007/978-981-10-6529-3 Library of Congress Control Number: 2017952914 © The Author(s) 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Brain Signals Processing (EEG) . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 The Necessity for Automated Classification . . . . . . . . . . . . . . 1.4 EEG Artifacts and Their Prevention . . . . . . . . . . . . . . . . . . . . 1.5 EEG Classification Methods (Literature Survey) . . . . . . . . . . . 1.6 Key Problems in EEG Classification Methods . . . . . . . . . . . . 1.7 A New Framework for Handling Uncertainty and—Artefacts in EEG Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 Analysis of Electroencephalogram (EEG) Using ANN . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Proposed System and Specification . . . . . . . . . . . . . . . . . . . . . 2.2.1 Digital Signal Transformation and Denoising . . . . . . . 2.2.2 Data Hiding and Retrieval . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Signal Compression . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Recognition of Brain Signals Using Neural Network . 2.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Neural Network Software . . . . . . . . . . . . . . . . . . . . . . 2.3.4 Current Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 The Inspiration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2.7 System Implementation. . . . . . . . . . . . . . . . . 2.7.1 Using Back Propagation Network . . . 2.7.2 The Pre-processing . . . . . . . . . . . . . . 2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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3 Classification and Analysis of EEG Using SVM and MRE . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Resources and Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Attainment of EEG Data . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Fuzzy System as a Pre Classifier . . . . . . . . . . . . . . . . . 3.2.3 Fuzzy Membership Functions . . . . . . . . . . . . . . . . . . . 3.2.4 Fuzzy Rule Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Estimation of Risk Level in Fuzzy Outputs. . . . . . . . . 3.2.6 Binary Representation of Risk Level Patterns . . . . . . . 3.2.7 Support Vector Machine as Post Classifier . . . . . . . . . 3.3 Support Vector Mechanism for Optimization of Fuzzy Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Minimum Relative Entropy (MRE) for Optimization of Fuzzy Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Algorithm for MRE Optimization . . . . . . . . . . . . . . . . 3.4 Result and Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Performance Index . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Quality Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4 Intelligent Technique to Identify Epilepsy Captures Using Fuzzy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Average Amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Rhythmicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Domain Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Fuzzy C-Means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Fuzzy Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.2 Motivation and Advantage of Using Fuzzy Logic . . . . 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contents
5 Analysis of EEG to Find Alzheimer’s Disease Using Intelligent Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Techniques and Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Signal Attainment and EEG Database . . . . . . . . . . . . . 5.2.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Neural Network Classifier . . . . . . . . . . . . . . . . . . . . . . 5.2.6 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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About the Authors
Dr. Sasikumar Gurumoorthy is a Professor in the Department of Computer Science and Systems Engineering, at Sree Vidyanikethan Engineering College in Tirupati. He is having 11 years of teaching and 7 years of research experience. He has held various senior positions such as Head of the Department, Chief Superintend, and Assistant Chief Superintend of University Exams. He also serves on the Board of Examiners and Board of Studies in Indian Universities. He has published over 75 research papers in different international journals and conferences, more in the area of intelligent system and interactive computing. He authored two reference text books, on “Programming in C and Introduction to Data Structures” in the area of UNIX and Windows operating system. For his outstanding contributions in the Wipro-Misson10X, he has been awarded In Pursuit of Excellence in Engineering Education through Innovation (in 2009). He has received Research Grant from DST-CSRI to work on “Intelligent System to Classify Human Brain Signals for Finding Brain Diseases.” His current interest includes soft computing and artificial intelligence in biomedical engineering, human and machine interaction and applications of intelligent system techniques, new user interface, brain-based interaction, human-centric computing, fuzzy sets and systems, image processing, cloud computing, content-based learning, and social network analysis.
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About the Authors
Dr. Naresh Babu Muppalaneni is an Associate Professor in the Department of Computer Science and Systems Engineering, at Sree Vidyanikethan Engineering College in Tirupati. He received his M.Tech. from Andhra University and Ph.D. from Acharya Nagarjuna University. He has published more than 15 papers in different international journals, conference proceedings, and edited research volumes. He has edited two research volumes for the Springer publications and has written a book on “Bioinformatics of Non Small Cell Lung Cancer and the Ras Proto-Oncogene” in Springer Briefs in Forensic and Medical Bioinformatics. He is in the editorial board, review panel of over three international journals. He is a life member of CSI, member of ISCA, IEEE. He is a recipient of Best Teacher Award from JNTU Kakinada. He has completed research projects worth of 1.6 crore from DST, DRDO. He has organized four international conferences and four workshops. His research interests are cryptography, artificial intelligence in biomedical engineering, human and machine interaction and applications of intelligent system techniques, social network analysis computational systems biology, bioinformatics.
Dr. Xiao-Zhi Gao received his B.Sc. and M.Sc. degrees from the Harbin Institute of Technology, China, in 1993 and 1996, respectively. He earned a D.Sc. (Tech.) degree from the Helsinki University of Technology, Finland, in 1999. Since January 2004, he has been working as a docent at the same university. He is also a guest professor of the Beijing Normal University, Harbin Institute of Technology, and Beijing City University, China. Dr. Gao has published more than 150 technical papers on refereed journals and international conferences. He is an Associate Editor of the Journal of Intelligent Automation and Soft Computing and an Editorial Board Member of the Journal of Applied Soft Computing, International Journal of Bio-Inspired Computation, and Journal of Hybrid Computing Research. Dr. Gao was the General Chair of the 2005 IEEE Mid-Summer Workshop on
About the Authors
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Soft Computing in Industrial Applications. His current research interests are neural networks, fuzzy logic, evolutionary computing, swarm intelligence, and artificial immune systems with their applications in industrial electronics.
