1st Edition

Epileptic Seizures and the EEG Measurement, Models, Detection and Prediction

    368 Pages 106 B/W Illustrations
    by CRC Press

    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.

    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


    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.