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

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

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Bibliographic Details
:
Varsavsky, Andrea.
TeilnehmendeR:
Mareels, Iven.
Cook, Mark.
Place / Publishing House:Milton : : Taylor & Francis Group,, 2010.
©2011.
Year of Publication:2010
Edition:1st ed.
Language:English
Online Access:
Physical Description:1 online resource (369 pages)
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Table of Contents:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • List of Figures
  • Preface
  • 1 Introduction
  • 1.1 The Brain and Epilepsy
  • 1.1.1 Micro-Scopic Dynamics: Single Neurons
  • 1.1.2 Meso/Macro-Scopic Dynamics: Neural Networks
  • 1.1.2.1 Cortico-Cortical Projections
  • 1.1.2.2 Thalamo-Cortical Projections
  • 1.1.3 Neurotransmitters and Neuromodulators
  • 1.1.4 Epilepsy - A Malfunctioning Brain
  • 1.1.4.1 Focal Epilepsy - Failure of Meso-Scopic Networks
  • 1.1.4.2 Non-Focal Epilepsy
  • 1.1.4.3 Continuous Epilepsy
  • 1.1.5 Diagnosis and Treatment of Epilepsy
  • 1.1.5.1 Anti-Epileptic Drugs
  • 1.1.5.2 Surgical Resection
  • 1.1.5.3 Electrical Stimulation
  • 1.2 The EEG - A Recording of the Brain
  • 1.2.1 The Normal EEG
  • 1.2.2 The Epileptic EEG
  • 1.2.3 Detecting Changes in the EEG
  • 1.3 Dynamics of the Brain
  • 1.3.1 Micro- and Macro-Scopic Models
  • 1.3.2 Dynamic Models of Epilepsy
  • 1.4 Stochasticity in Neural Systems
  • 1.5 Conclusions and Further Reading
  • 2 EEG Generation and Measurement
  • 2.1 Principles of Bioelectric Phenomena
  • 2.1.1 A Foreword on Notation
  • 2.1.2 From Single Charges to Equivalent Dipoles
  • 2.1.3 Equivalent Current Dipoles
  • 2.1.4 Macro-Scopic Mean Fields - Homogeneous Media
  • 2.1.5 Macro-Scopic Mean Fields - Inhomogeneous Media
  • 2.2 Current Sources in Biological Tissue
  • 2.2.1 Synaptic Structure and Current Dipoles
  • 2.2.2 Spatial Integration
  • 2.2.2.1 Cortical Structure
  • 2.2.2.2 Cortical Folds
  • 2.2.3 Temporal Integration
  • 2.3 Volume Conducting Properties of the Head
  • 2.3.1 Head Geometry
  • 2.3.2 Capacitive Effects of Tissue
  • 2.3.3 Estimating Conductivities
  • 2.3.3.1 Brain
  • 2.3.3.2 CSF
  • 2.3.3.3 Skull
  • 2.3.3.4 Scalp
  • 2.4 The EEG: A Macro-Scopic View of the Brain
  • 2.4.1 EEG Measurement
  • 2.4.1.1 Cortical (Intra-Cranial) Recordings
  • 2.4.1.2 Scalp Recordings.
  • 2.4.1.3 The Search for an Ideal Reference
  • 2.4.1.4 Spatial Filtering Properties of the Skull
  • 2.4.2 EEG Dynamics
  • 2.4.3 Epilepsy and the EEG
  • 2.5 Conclusions
  • 2.A Units of Electric Quantities
  • 2.B Volume Conductor Boundary Conditions
  • 2.C Capacitance in RC Circuits
  • 3 Signal Processing in EEG Analysis
  • 3.1 Mathematical Representation of the EEG
  • 3.2 Preprocessing
  • 3.3 Feature Extraction
  • 3.3.0.1 Computing Statistics: Averages vs. Instances
  • 3.3.0.2 Noise
  • 3.3.0.3 Stationarity and Windowing
  • 3.3.0.4 Linearity, Non-Linearity, Determinism and Stochasticity
  • 3.3.0.5 Normalization
  • 3.3.1 Time Domain Analysis
  • 3.3.1.1 Signal Amplitude (Energy) and Variance (Power)
  • 3.3.1.2 Periodicity (Auto-Correlation)
  • 3.3.1.3 Synchronization
  • 3.3.2 Frequency Domain Analysis
  • 3.3.3 Time-Frequency Analysis
  • 3.3.4 Non-Linear Analysis
  • 3.3.4.1 Embedding Theory
  • 3.3.4.2 Dimension - How Complex is a System?
  • 3.3.4.3 Lyapunov Exponents - How Predictable is a System?
  • 3.3.4.4 Entropy - How Random is the System?
  • 3.3.4.5 Non-Linear Dynamics and Analysis of the Epileptic EEG
  • 3.4 Detection and Prediction of Seizures in Literature
  • 3.5 Conclusions
  • 4 Classifying the EEG
  • 4.1 Types of Classifiers
  • 4.1.1 Association Rules
  • 4.1.2 Artificial Neural Networks
  • 4.1.3 Support Vector Machines
  • 4.2 Expert System
  • 4.2.1 Processing Decisions
  • 4.2.2 Spatio-Temporal Context
  • 4.2.3 Patient Specificity
  • 4.3 Conclusions
  • 5 Seizure Detection
  • 5.1 The Problem of Seizure Detection
  • 5.1.1 The EEG Database
  • 5.1.1.1 Group 1 - Scalp EEG Data (&lt
  • 6 Seizures per Patient)
  • 5.1.1.2 Group 2 - Scalp EEG Data (6 - 10 Seizures per Patient)
  • 5.1.1.3 Group 3 - Scalp EEG Data, Non-Epileptic Patients
  • 5.1.1.4 Group 4 - Intra-Cranial EEG Data
  • 5.1.2 Performance Evaluation Metrics.
  • 5.2 Evaluation of Classification Methods
  • 5.2.1 Feature Extraction
  • 5.2.2 ANN Training and Testing
  • 5.2.3 SVM Training and Testing
  • 5.2.4 Results and Comparisons
  • 5.3 Evaluation of Patient Un-Specific Seizure Detectors
  • 5.3.1 Algorithm 1: Monitor
  • 5.3.1.1 Algorithm Description
  • 5.3.1.2 Results
  • 5.3.2 Algorithm 2: CNet
  • 5.3.2.1 Algorithm Description
  • 5.3.2.2 Results
  • 5.3.3 Algorithm 3: Reveal
  • 5.3.3.1 Algorithm Description
  • 5.3.3.2 Results
  • 5.3.4 Algorithm 4: Saab
  • 5.3.4.1 Algorithm Description
  • 5.3.4.2 Results
  • 5.3.5 Comparisons and Conclusions
  • 5.4 Evaluation of Onset Seizure Detectors
  • 5.4.1 Feature Extraction
  • 5.4.1.1 Cross Correlation (XCORR)
  • 5.4.1.2 Power Spectral Density (PSD)
  • 5.4.1.3 Wavelet Analysis (WAV)
  • 5.4.1.4 Correlation Dimension (CD)
  • 5.4.2 Results and Comparisons
  • 5.5 Conclusions
  • 6 Modeling for Epilepsy
  • 6.1 Physiological Parameters of Neural Models
  • 6.1.1 Parameters in Single Neurons
  • 6.1.2 Parameters in Networks of Neurons
  • 6.2 Micro-Scopic (Statistical) Models
  • 6.2.1 Model Summary
  • 6.2.2 Validation and Limitations
  • 6.3 Meso-Scopic (Phenomenological) Models
  • 6.3.1 Model Summary
  • 6.3.2 Analysis: Linearization, Stability and Instability
  • 6.3.3 Validation and Limitations: Rhythms in the EEG
  • 6.3.3.1 Simulating the Normal EEG
  • 6.3.3.2 Simulating the Seizure EEG
  • 6.3.3.3 Caution
  • 6.3.4 Relationship to Micro-Scopic Models
  • 6.4 Macro-Scopic Models (Future Outlook)
  • 6.5 Practical Use of Models
  • 6.5.1 Epileptic Seizure Generation
  • 6.5.1.1 Seizure Initiation
  • 6.5.1.2 Seizure Termination by Electrical Stimulation
  • 6.5.2 Limitations of the EEG
  • 6.6 Conclusions
  • 6.A Physiological Parameters and Notation
  • 6.B Summary of IF Model
  • 6.C Summary of Phenomenological Model
  • 7 On the Predictability of Seizures.
  • 7.1 Predictability - Terminology Made Clear
  • 7.2 How to Estimate LRD
  • 7.2.1 Example Distributions
  • 7.2.2 Computing α
  • 7.2.3 Simulations
  • 7.2.4 Results
  • 7.3 Seizure Frequency Dataset
  • 7.4 Analysis - Estimation of α
  • 7.5 Memory and Predictability of Seizures
  • 7.6 Conclusions
  • 8 Concluding Remarks
  • Glossary
  • Bibliography
  • Index.

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