Epileptic Seizures and the EEG : Measurement, Models, Detection and Prediction book cover
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Epileptic Seizures and the EEG
Measurement, Models, Detection and Prediction



ISBN 9781439812006
Published December 21, 2010 by CRC Press
368 Pages 106 B/W Illustrations

 
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Book 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


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Author(s)

Biography

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.

Support Material

Open Access Content

Open Access content has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CCBY-NC-ND) license

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