Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology, 1st Edition (Paperback) book cover

Automated EEG-Based Diagnosis of Neurological Disorders

Inventing the Future of Neurology, 1st Edition

By Hojjat Adeli, Samanwoy Ghosh-Dastidar

CRC Press

423 pages | 103 B/W Illus.

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pub: 2010-02-09
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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.

Table of Contents

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



About the Authors

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.

Subject Categories

BISAC Subject Codes/Headings:
MEDICAL / Biotechnology
SCIENCE / Life Sciences / Neuroscience