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 |
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Varsavsky, Andrea. Epileptic Seizures and the EEG : Measurement, Models, Detection and Prediction. 1st ed. Milton : Taylor & Francis Group, 2010. ©2011. 1 online resource (369 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Mareels, Iven. Cook, Mark. Print version: Varsavsky, Andrea Epileptic Seizures and the EEG Milton : Taylor & Francis Group,c2010 9781439812006 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=7245523 Click to View |
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Varsavsky, Andrea. |
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Varsavsky, Andrea. Epileptic Seizures and the EEG : Measurement, Models, Detection and Prediction. 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. |
author_facet |
Varsavsky, Andrea. Mareels, Iven. Cook, Mark. |
author_variant |
a v av |
author2 |
Mareels, Iven. Cook, Mark. |
author2_variant |
i m im m c mc |
author2_role |
TeilnehmendeR TeilnehmendeR |
author_sort |
Varsavsky, Andrea. |
title |
Epileptic Seizures and the EEG : Measurement, Models, Detection and Prediction. |
title_sub |
Measurement, Models, Detection and Prediction. |
title_full |
Epileptic Seizures and the EEG : Measurement, Models, Detection and Prediction. |
title_fullStr |
Epileptic Seizures and the EEG : Measurement, Models, Detection and Prediction. |
title_full_unstemmed |
Epileptic Seizures and the EEG : Measurement, Models, Detection and Prediction. |
title_auth |
Epileptic Seizures and the EEG : Measurement, Models, Detection and Prediction. |
title_new |
Epileptic Seizures and the EEG : |
title_sort |
epileptic seizures and the eeg : measurement, models, detection and prediction. |
publisher |
Taylor & Francis Group, |
publishDate |
2010 |
physical |
1 online resource (369 pages) |
edition |
1st ed. |
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. |
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Electronic books. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>07513nam a22004333i 4500</leader><controlfield tag="001">5007245523</controlfield><controlfield tag="003">MiAaPQ</controlfield><controlfield tag="005">20240229073848.0</controlfield><controlfield tag="006">m o d | </controlfield><controlfield tag="007">cr cnu||||||||</controlfield><controlfield tag="008">240229s2010 xx o ||||0 eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781000218923</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9781439812006</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(MiAaPQ)5007245523</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(Au-PeEL)EBL7245523</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1378936199</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">MiAaPQ</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">MiAaPQ</subfield><subfield code="d">MiAaPQ</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">616.85307547</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Varsavsky, Andrea.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Epileptic Seizures and the EEG :</subfield><subfield code="b">Measurement, Models, Detection and Prediction.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Milton :</subfield><subfield code="b">Taylor & Francis Group,</subfield><subfield code="c">2010.</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2011.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (369 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="590" ind1=" " ind2=" "><subfield code="a">Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. </subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic books.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mareels, Iven.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cook, Mark.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Varsavsky, Andrea</subfield><subfield code="t">Epileptic Seizures and the EEG</subfield><subfield code="d">Milton : Taylor & Francis Group,c2010</subfield><subfield code="z">9781439812006</subfield></datafield><datafield tag="797" ind1="2" ind2=" "><subfield code="a">ProQuest (Firm)</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=7245523</subfield><subfield code="z">Click to View</subfield></datafield></record></collection> |