1st Edition

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

    424 Pages 103 B/W Illustrations
    by CRC Press

    423 Pages 103 B/W Illustrations
    by CRC Press

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    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.

    Basic Concepts

    Time-Frequency Analysis: Wavelet Transforms
    Signal Digitization and Sampling
    Time and Frequency Domain Analyses
    Time-Frequency Analysis
    Types of Wavelets
    Advantages of the Wavelet Transform

    Chaos Theory
    Attractors in Chaotic Systems

    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 EEGs
    Epilepsy and Epileptic Seizures

    Analysis of EEGs in an Epileptic Patient Using Wavelet Transform
    Wavelet Analysis of a Normal
    Characterization of the 3-Hz Spike and Slow Wave Complex in
    Absence Seizures Using Wavelet
    Concluding Remarks

    Wavelet-Chaos Methodology for Analysis of EEGs and EEG Sub-Bands
    Wavelet-Chaos Analysis of EEG
    Application and Results
    Concluding Remarks

    Mixed-Band Wavelet-Chaos Neural Network Methodology
    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
    Principal Component Analysis for Feature
    Cosine Radial Basis Function Neural Network: EEG Classification
    Applications 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
    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
    Description of the EEG
    Complexity and Chaoticity of the EEG: Results of the
    Three-Way Factorial ANOVA
    Concluding Remarks

    Third Generation Neural Networks: Spiking Neural Networks
    Spiking Neural Networks: Spiking Neurons and Learning Algorithms
    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
    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




    Hojjat Adeli is the Abba G. Lichtenstein Professor at The Ohio State University, Editor-in-Chief of the International Journal of Neural Systems, and author of 14 pioneering books. Samanwoy Ghosh-Dastidar is Principal Biomedical Engineer at ANSAR Medical Technologies in Philadelphia. Nahid Dadmehr is a board-certified neurologist in practice in Ohio since 1991.