Automated EEG-Based Diagnosis of Neurological Disorders : Inventing the Future of Neurology

Based on the authors’ groundbreaking research, Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology presents a research ideology, a novel multi-paradigm methodology, and advanced computational models for the automated EEG-based diagnosis of neurological disorders. It is based on the ingenious integration of three different computing technologies and problem-solving paradigms: neural networks, wavelets, and chaos theory. The book also includes three introductory chapters that familiarize readers with these three distinct paradigms. After extensive research and the discovery of relevant mathematical markers, the authors present a methodology for epilepsy diagnosis and seizure detection that offers an exceptional accuracy rate of 96 percent. They examine technology that has the potential to impact and transform neurology practice in a significant way. They also include some preliminary results towards EEG-based diagnosis of Alzheimer’s disease. The methodology presented in the book is especially versatile and can be adapted and applied for the diagnosis of other brain disorders. The senior author is currently extending the new technology to diagnosis of ADHD and autism. A second contribution made by the book is its presentation and advancement of Spiking Neural Networks as the seminal foundation of a more realistic and plausible third generation neural network. Cover......Page 1 Title Page......Page 3 ISBN 9781439815311......Page 4 Preface......Page 6 Acknowledgments......Page 9 About the Authors......Page 10 List of Figures......Page 12 List of Tables......Page 21 Contents......Page 24 I. Basic Concepts......Page 32 1. Introduction......Page 34 2.1 Signal Digitization and Sampling Rate......Page 36 2.2 Time and Frequency Domain Analyses......Page 38 2.3.1 Short Time Fourier Transform (STFT)......Page 42 2.3.2 Wavelet Transform......Page 43 2.4 Types of Wavelets......Page 51 2.5 Advantages of the Wavelet Transform......Page 57 3.1 Introduction......Page 60 3.2 Attractors in Chaotic Systems......Page 62 3.3.1 Measures of Chaos......Page 71 3.3.2 Preliminary Chaos Analysis - Lagged Phase Space......Page 72 3.3.3 Final Chaos Analysis......Page 76 4.1 Data Classification......Page 80 4.2 Cluster Analysis......Page 81 4.3 k-Means Clustering......Page 85 4.4 Discriminant Analysis......Page 86 4.5 Principal Component Analysis......Page 87 4.6 Artificial Neural Networks......Page 89 4.6.1 Feed forward Neural Network and Error Backpropagation......Page 90 4.6.2 Radial Basis Function Neural Network......Page 97 II. Automated EEG-Based Diagnosis of Epilepsy......Page 100 5.1 Spatio-Temporal Activity in the Human Brain......Page 102 5.2 EEG: A Spatio-Temporal Data Mine......Page 103 5.3 Data Mining Techniques......Page 109 5.4.1 Feature Space Identification and Feature EnhancementUsing Wavelet-Chaos Methodology......Page 112 5.4.2 Development of Accurate and Robust Classifiers......Page 115 5.5 Epilepsy and Epileptic Seizures......Page 116 6.1 Introduction......Page 120 6.2 Wavelet Analysis of a Normal EEG......Page 124 6.3.1 Daubechies Wavelets......Page 127 6.3.2 Harmonic Wavelet......Page 128 6.3.3 Characterization......Page 137 6.4 Concluding Remarks......Page 147 7.1 Introduction......Page 150 7.2 Wavelet-Chaos Analysis of EEG Signals......Page 153 7.3.1 Description of the EEG Data Used in the Research......Page 156 7.3.2 Data Preprocessing and Wavelet Decomposition of EEG into Sub-Bands......Page 159 7.3.3 Results of Chaos Analysis for a Sample Set of Unfiltered EEGs......Page 160 7.3.4 Statistical Analysis of Results for All EEGs......Page 164 7.4 Concluding Remarks......Page 169 8.1 Introduction......Page 174 8.2 Wavelet-Chaos Analysis: EEG Sub-Bands and Feature Space Design......Page 175 8.3 Data Analysis......Page 177 8.4.1 k-Means Clustering......Page 179 8.4.2 Discriminant Analysis......Page 181 8.4.3 RBFNN......Page 184 8.4.4 LMBPNN......Page 186 8.5 Mixed-Band Analysis: Wavelet-Chaos-Neural Network......Page 187 8.6 Concluding Remarks......Page 191 9.1 Introduction......Page 194 9.2 Principal Component Analysis for Feature Enhancement......Page 196 9.3 Cosine Radial Basis Function Neural Network: EEG Classification......Page 200 9.4.2 Output Encoding Scheme......Page 203 9.4.3 Comparison of Classifiers......Page 204 9.4.4 Sensitivity to Number of Eigenvectors......Page 207 9.4.5 Sensitivity to Training Size......Page 208 9.4.6 Sensitivity to Spread......Page 209 9.5 Concluding Remarks and Clinical Significance......Page 212 III. Automated EEG-Based Diagnosis of Alzheimer's Disease......Page 214 10.1 Introduction......Page 216 10.2 Neurological Markers of Alzheimer's Disease......Page 218 10.3.1 Anatomical Imaging versus Functional Imaging......Page 222 10.3.2 Identification of Region of Interest (ROI)......Page 224 10.3.3 Image Registration Techniques......Page 225 10.3.4 Linear and Area Measures......Page 226 10.3.5 Volumetric Measures......Page 227 10.4 Classification Models......Page 228 10.5.1 Approaches to Neural Modeling......Page 231 10.5.2 Hippocampal Models of Associative Memory......Page 233 10.5.3 Neural Models of Progression of AD......Page 234 11.1 EEGs for Diagnosis and Detection of Alzheimer's Disease......Page 238 11.2 Time-Frequency Analysis......Page 239 11.3 Wavelet Analysis......Page 243 11.4 Chace Analysis......Page 244 11.5 Concluding Remarks......Page 249 12.1 Introduction......Page 252 12.2.1 Description of the EEG Data......Page 255 12.2.3 Chaos Analysis and ANOVA Design......Page 257 12.3.1 Complexity and Chaoticity of the EEG: Results of the Three-Way Factorial ANOVA......Page 260 12.3.3 Local Complexity and Chaoticity......Page 261 12.4 Discussion......Page 0 12.4.1 Chaoticity versus Complexity......Page 262 12.4.2 Eyes Open versus Eyes Closed......Page 265 12.5 Concluding Remarks......Page 267 IV. Third Generation Neural Networks: Spiking Neural Networks......Page 270 13.1 Introduction......Page 272 13.2 Information Encoding and Evolution of Spiking Neurons......Page 274 13.3 Mechanism of Spike Generation in Biological Neurons......Page 277 13.4 Models of Spiking Neurons......Page 283 13.5 Spiking Neural Networks (SNNs)......Page 285 13.6 Unsupervised Learning......Page 287 13.7 Supervised Learning......Page 289 13.7.1 Feedforward Stage: Computation of Spike Times and Network Error......Page 295 13.7.2 Backpropagation Stage: Learning Algorithms......Page 297 14.1.1 Number of Neurons in Each Layer......Page 302 14.1.3 Initialization of Weights......Page 303 14.1.4 Heuristic Rules for SNN Learning Algorithms......Page 304 14.2.1 Input and Output Encoding......Page 306 14.2.2 SNN Architecture......Page 307 14.2.4 Type of Neuron (Excitatory or Inhibitory)......Page 309 14.2.5 Convergence Results for a Simulation Time of 50 ms......Page 310 14.2.6 Convergence Results for a Simulation Time of 25 ms......Page 316 14.3.1 Input Encoding......Page 319 14.3.2 Output Encoding......Page 322 14.4.3 Convergence Criteria: MSE and Training Accuracy......Page 330 14.3.4 Convergence Criteria: MSE and Training Accuracy......Page 323 14.3.5 Heuristic Rules for Adaptive Simulation Time and SpikeProp Learning Rate......Page 325 14.3.6 Classification Accuracy and Computational Efficiency versus Training Size......Page 326 14.3.7 Summary......Page 328 14.4.1 Input and Output Encoding......Page 329 14.4.4 Classification Accuracy versus Training Size and Number of Input Neurons......Page 331 14.4.5 Classification Accuracy versus Desired Training Accuracy 301......Page 332 14.4.6 Summary......Page 333 14.5 Concluding Remarks......Page 334 15.1 Introduction......Page 336 15.2.1 MuSpiNN Architecture......Page 341 15.2.2 Multi-Spiking Neuron and the Spike Response Model......Page 343 15.3.1 MuSpi NN Error Function......Page 348 15.3.2 Error Backpropagation for Adjusting Synaptic Weights......Page 349 15.3.3 Gradient Computation for Synapses Between a Neuron in the Last Hidden Layer and a Neuron in the Output Layer......Page 350 15.3.4 Gradient Computation for Synapses Between a Neuronin the Input or Hidden Layer and a Neuron in the Hidden Layer......Page 355 16.1 Parameter Selection and Weight Initialization......Page 360 16.2 Heuristic Rules for Multi-Spike Prop......Page 362 16.3 XOR Problem......Page 363 16.4 Fisher Iris Classification Problem......Page 365 16.5 EEG Classification Problem......Page 369 16.6 Discussion and Concluding Remarks......Page 370 17. The Future......Page 378 Bibliography......Page 380 Index......Page 414 Back Page......Page 419

inventing The Future Of Neurology Based On The Authors’ Groundbreaking Research, This Book Presents A Research Ideology, A Novel Multi-paradigm Methodology, And Advanced Computational Models For The Automated Eeg-based Diagnosis Of Neurological Disorders. It Is Based On The Ingenious Integration Of Three Different Computing Technologies And Problem-solving Paradigms: Neural Networks, Wavelets, And Chaos Theory. The Book Also Includes Three Introductory Chapters That Familiarize The Readers With These Three Distinct Paradigms.

after Extensive Research And The Discovery Of Relevant Mathematical Markers, The Authors Present A Methodology For Epilepsy Diagnosis And Seizure Detection That Offers An Exceptional Accuracy Rate Of 96 Percent.

the Technology Presented In The Book Has The Potential To Impact And Transform Neurology Practice In A Significant Way. The Book Also Includes Some Preliminary Results Towards Eeg-based Diagnosis Of Alzheimer’s Disease. The Methodology Presented In The Book Is Especially Versatile And Can Be Adapted And Applied For The Diagnosis Of Other Brain Disorders. The Senior Author Is Currently Extending The New Technology To Diagnosis Of Adhd And Autism. A Second Contribution Made By The Book Is Its Presentation And Advancement Of Spiking Neural Networks As The Seminal Foundation Of A More Realistic And Plausible Third Generation Neural Network.

Time-frequency analysis : wavelet transforms Chaos theory Classifier designs Electroencephalograms and epilepsy Analysis of EEGs in an epileptic patient using wavelet transform Wavelet-chaos methodology for analysis of EEGs and EEG sub-bands Mixed-band wavelet-chaos neural network methodology Principal component analysis-enhanced cosine radial basis function neural network Alzheimer's disease and models of computation : imaging, classification, and neural models Alzheimer's disease : models of computation and analysis of EEGs A spatio-temporal wavelet-chaos methodology for EEG based diagnosis of Alzheimer's disease Spiking neural networks : spiking neurons and learning algorithms Improved spiking neural networks with application to EEG classification and epilepsy and seizure detection A new supervised learning algorithm for multiple spiking neural networks Applications of multiple spiking neural networks : EEG classification and epilepsy and seizure detection. Inventing the Future of Neurology based on the authors' groundbreaking research, this book presents a research ideology, a novel multi-paradigm methodology, and advanced computational models for the automated EEG-based diagnosis of neurological disorders. It is based on the ingenious integration of three different computing technologies and problem-solving paradigms: neural networks, wavelets, and chaos theory. The book also includes three introductory chapters that familiarize the readers with these three distinct paradigms. After extensive research and the discovery of relevant mathematical Presents a novel approach for automated EEG-based diagnosis of neurological disorders such as epilepsy. This book introduces three computing and problem solving paradigms: neural networks, wavelets, and chaos theory, along with many mathematical algorithms. It concludes with applications of the method to epilepsy and to Alzheimer's disease.