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