Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction, 1st Edition (Hardback) book cover

Epileptic Seizures and the EEG

Measurement, Models, Detection and Prediction, 1st Edition

By Andrea Varsavsky, Iven Mareels, Mark Cook

CRC Press

370 pages | 106 B/W Illus.

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pub: 2010-12-21
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Description

Analysis of medical data using engineering tools is a rapidly growing area, both in research and in industry, yet few texts exist that address the problem from an interdisciplinary perspective. Epileptic Seizures and the EEG: Measurement, Models, Detection and Prediction brings together biology and engineering practices and identifies the aspects of the field that are most important to the analysis of epilepsy.

Analysis of EEG records

The book begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG as a measured signal and explains its use in the study of epilepsy. Next, it provides an explanation of the type of brain activity likely to register in EEG measurements, offering quantitative analysis of the populations of neurons that contribute to both scalp and cortical EEG and discussing the limitations and effects that choices made in the recording process have on the data. The book provides an overview of how these EEG records are and have been analyzed in the past, concentrating on the mathematics relevant to the problem of classification of EEG. The authors use these extracted features to differentiate between or classify inter-seizure, pre-seizure and seizure EEG.

The challenge of seizure prediction

The book focuses on the problem of seizure detection and surveys the physiologically based dynamic models of brain activity. Finally, the book addresses the fundamental question: can seizures be predicted? Through analysis of epileptic activity spanning from 3 hours to 25 years, it is proposed that seizures may be predictable, but the amount of data required is greater than previously thought. Based on the authors’ extensive research, the book concludes by exploring a range of future possibilities in seizure prediction.

Table of Contents

Introduction

The Brain and Epilepsy

Micro-scopic Dynamics: Single Neurons

Meso/Macro-scopic Dynamics: Neural Networks

Neurotransmitters and Neuromodulators

Epilepsy - A Malfunctioning Brain

Diagnosis and Treatment of Epilepsy

The EEG - A Recording of the Brain

The Normal EEG

The Epileptic EEG

Detecting Changes in the EEG

Dynamics of the Brain

Micro- and Macro-scopic models

Dynamic Models of Epilepsy

Stochasticity in Neural Systems

EEG Generation and Measurement

Principles of Bioelectric Phenomena

A Foreword On Notation

From Single Charges to Equivalent Dipoles

Equivalent Current Dipoles

Macro-scopic Mean Fields - Homogeneous Media

Macro-scopic Mean Fields - Inhomogeneous Media

Current Sources in Biological Tissue

Synaptic Structure and Current Dipoles

Spatial Integration

Temporal Integration

Volume Conducting Properties of the Head

Head Geometry

Capacitive Effects of Tissue

Estimating Conductivities

The EEG: A Macro-scopic View of the Brain

EEG Measurement

EEG Dynamics

Epilepsy and the EEG

Appendix A: Units of Electric Quantities

Appendix B: Volume Conductor Boundary Conditions

Appendix:C: Capacitance in RC Circuits

Signal Processing in EEG Analysis

Mathematical Representation of the EEG

Preprocessing

Feature Extraction

Time Domain Analysis

Frequency Domain Analysis

Time-Frequency Analysis

Nonlinear Analysis

Detection and Prediction of Seizures in Literature

Classifying the EEG

Types of Classifiers

Association Rules

Artiificial Neural Networks

Support Vector Machines

Expert System

Processing Decisions

Spatio-Temporal Context

Patient Specificity

Seizure Detection

The Problem of Seizure Detection

The EEG Database

Performance Evaluation Metrics

Evaluation of Classification Methods

Feature Extraction

ANN Training and Testing

SVM Training and Testing

Results and Comparisons

Evaluation of Patient Un-specific Seizure Detectors

Algorithm 1: Monitor

Algorithm 2: CNet

Algorithm 3: Reveal

Algorithm 4: Saab

Comparisons and Conclusions

Evaluation of Onset Seizure Detectors

Feature Extraction

Results and Comparisons

Modeling for Epilepsy

Physiological Parameters of Neural Models

Parameters in Single Neurons

Parameters in Networks of Neurons

Micro-scopic (Statistical) Models

Model Summary

Validation and Limitations

Meso-scopic (Phenomenological) Models

Model Summary

Analysis: Linearization, Stability and Instability

Validation and Limitations: Rhythms in the EEG

Relationship to Micro-scopic Models

Macro-scopic Models (Future Outlook)

Practical Use of Models

Epileptic Seizure Generation

Limitations of the EEG

Appendix A: Physiological Parameters and Notation

Appendix B: Summary of IF Model

Appendix C: Summary of Phenomenological Model

On the Predictability of Seizures

Predictability - Terminology Made Clear

How to Estimate LRD

Example Distributions

Computing α

Simulations

Results

Seizure Frequency Dataset

Analysis - Estimation of α

Memory and Predictability of Seizures

Concluding Remarks

About the Authors

Andrea Varsavsky and Iven Mareels are with The University of Melbourne in Victoria, Australia. Mark Cook is with St. Vincent’s Hospital and the University of Melbourne in Victoria, Australia.

Subject Categories

BISAC Subject Codes/Headings:
MED009000
MEDICAL / Biotechnology
SCI089000
SCIENCE / Life Sciences / Neuroscience