Automated Eeg-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology (Hardcover)

Basic Concepts Introduction Time-Frequency Analysis: Wavelet Transforms Signal Digitization and Sampling Time and Frequency Domain Analyses Time-Frequency AnalysisTypes of Wavelets Advantages of the Wavelet Transform Chaos Theory Introduction Attractors in Chaotic SystemsChaos Classifier Designs Data Classification Cluster Analysis k-Means Clustering Discriminant Analysis Principal Component Artificial Neural Networks Automated EEG-Based Diagnosis of Epilepsy Electroencephalograms and Epilepsy Spatio-Temporal Activity in the Human EEG: A Spatio-Temporal Data Data Mining Techniques Multi-Paradigm Data Mining Strategy for EEGsEpilepsy and Epileptic Seizures Analysis of EEGs in an Epileptic Patient Using Wavelet Transform Introduction Wavelet Analysis of a Normal Characterization of the 3-Hz Spike and Slow Wave Complex inAbsence Seizures Using Wavelet Concluding Remarks Wavelet-Chaos Methodology for Analysis of EEGs and EEG Sub-Bands IntroductionWavelet-Chaos Analysis of EEG Application and Results Concluding Remarks Mixed-Band Wavelet-Chaos Neural Network Methodology Introduction Wavelet-Chaos Analysis: EEG Sub-Bands and Feature Space Design Data Analysis Band-Specific Analysis: Selecting Classifiers and Feature Mixed-Band Analysis: Wavelet-Chaos-Neural Network Concluding Remarks Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network Introduction Principal Component Analysis for Feature Cosine Radial Basis Function Neural Network: EEG ClassificationApplications and Results Concluding Remarks and Clinical Significance Automated EEG-Based Diagnosis of Alzheimer’s Disease Alzheimer’s Disease and Models of Computation: Imaging, Classification, and Neural Models Introduction Neurological Markers of Alzheimer’s Imaging Studies Classification Models . Neural Models of Memory and Alzheimer’s Disease Approaches to Neural Modeling Alzheimer’s Disease: Models of Computation and Analysis of EEGs EEGs for Diagnosis and Detection of Alzheimer’s Disease Time-Frequency Analysis Wavelet Analysis Chaos Analysis Concluding Remarks A Spatio-Temporal Wavelet-Chaos Methodology for EEG Based Diagnosis of Alzheimer’s Disease IntroductionMethodology Description of the EEG Results Complexity and Chaoticity of the EEG: Results of theThree-Way Factorial ANOVA Discussion Concluding Remarks Third Generation Neural Networks: Spiking Neural Networks Spiking Neural Networks: Spiking Neurons and Learning Algorithms Introduction Information Encoding and Evolution of Spiking Mechanism of Spike Generation in Biological Neurons Models of Spiking Neurons Spiking Neural Networks (SNNs) Unsupervised Learning Supervised Learning Improved Spiking Neural Networks with Application to EEG Classification and Epilepsy and Seizure Detection XOR Classification Problem Fisher Iris Classification Problem EEG Classification Problem Input and Output Encoding Concluding Remarks A New Supervised Learning Algorithm for Multiple Spiking Neural Networks Introduction Multi-Spiking Neural Network (MuSpiNN) and Neuron Model Multi-SpikeProp: Backpropagation Learning Algorithm for MuSpiNN Applications of Multiple Spiking Neural Networks: EEG Classification and Epilepsy and Seizure Detection Parameter Selection and Weight Initialization Heuristic Rules for Multi-SpikeProp XOR Problem Fisher Iris Classification Problem EEG Classification Problem Discussion and Concluding Remarks The Future Bibliography Index