دانلود کتاب Practical Biomedical Signal Analysis Using MATLAB®

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Cover Half Title Series Page Title Page Copyright Page Contents About the Series Preface List of Abbreviations 1. A Short Introduction to MATLAB® 1.1. Introduction 1.2. Where Is Help? 1.3. Vectors and Matrixes 1.4. Matrix Operations 1.4.1. Algebraic Operations 1.4.2. Matrix Indexing 1.4.3. Logical Indexing 1.4.4. Example Exercise 1.5. Conditionals 1.6. Loops 1.7. Scripts and Functions 1.8. Working with Binary Files 1.8.1. Saving to and Loading from Binary Files 1.8.2. Saving and Loading Signals Using .mat Files 1.8.3. Exercises 1.8.3.1. Unknown Data Type 1.8.3.2. Unknown Number of Channels 2. Introductory Concepts 2.1. Stochastic and Deterministic Signals, Concepts of Stationarity and Ergodicity 2.2. Discrete Signals 2.2.1. The Sampling Theorem 2.2.1.1. Aliasing 2.2.2. Quantization Error 2.3. Linear Time Invariant Systems 2.4. Duality of Time and Frequency Domains 2.4.1. Continuous Periodic Signal 2.4.2. Infinite Continuous Signal 2.4.3. Finite Discrete Signal 2.4.4. Basic Properties of Fourier Transform 2.4.5. Power Spectrum: The Plancherel Theorem and Parseval's Theorem 2.4.6. Z-Transform 2.4.7. Uncertainty Principle 2.5. Hypotheses Testing 2.5.1. The Null and Alternative Hypothesis 2.5.2. Types of Tests 2.5.3. Multiple Comparisons Problem 2.5.3.1. Correcting the Signi cance Level 2.5.3.2. Parametric and Non-Parametric Statistical Maps 2.5.3.3. False Discovery Rate 2.6. Surrogate Data Techniques 3. Single Channel (Univariate) Signal 3.1. Filters 3.1.1. Designing Filters 3.1.2. Changing the Sampling Frequency 3.1.3. Matched Filters 3.1.4. Wiener Filter 3.2. Probabilistic Models 3.2.1. Hidden Markov Model 3.2.2. Kalman Filters 3.3. Stationary Signals 3.3.1. Analytic Tools in the Time Domain 3.3.1.1. Mean Value, Amplitude Distributions 3.3.1.2. Entropy and Information Measure 3.3.1.3. Autocorrelation Function 3.3.2. Analytic Tools in the Frequency Domain 3.3.2.1. Estimators of Spectral Power Density Based on Fourier Transform 3.3.2.2. Choice of Windowing Function 3.3.2.3. Parametric Models: AR, ARMA 3.4. Non-Stationary Signals 3.4.1 Instantaneous Amplitude and Instantaneous Frequency 3.4.2. Analytic Tools in the Time-Frequency Domain 3.4.2.1. Time-Frequency Energy Distributions 3.4.2.2. Time-Frequency Signal Decompositions 3.4.3. Cross-Frequency Coupling 3.4.3.1. Models of Phase-Amplitude Coupling 3.4.3.2. Evaluation of Phase-Amplitude Coupling 3.5. Non-Linear Methods of Signal Analysis 3.5.1. Lyapunov Exponent 3.5.2. Correlation Dimension 3.5.3. Detrended Fluctuation Analysis 3.5.4. Recurrence Plots 3.5.5. Poincare Map 3.5.6. Approximate, Sample, and Multiscale Entropy 3.5.7. Limitations of Non-Linear Methods 4. Multiple Channels (Multivariate) Signals 4.1. Cross-Estimators: Cross-Correlation, Cross-Spectra, Coherence 4.2. Multivariate Autoregressive Model (MVAR) 4.2.1. Formulation of MVAR Model 4.2.2. MVAR in the Frequency Domain 4.3. Measures of Directedness 4.3.1. Estimators Based on the Phase Difference 4.3.2. Causality Measures 4.3.2.1. Granger Causality 4.3.2.2. Granger Causality Index and Granger-Geweke Causality 4.3.2.3. Directed Transfer Function 4.3.2.4. Partial Directed Coherence 4.3.2.5. Directed Coherence 4.4. Non-Linear Estimators of Dependencies between Signals 4.4.1. Kullback-Leibler Entropy, Mutual Information 4.4.2. Transfer Entropy 4.4.3. Generalized Synchronization and Synchronization Likelihood 4.4.4. Phase Synchronization (Phase Locking Value) 4.4.5. Testing the Reliability of the Estimators of Directedness 4.5. Comparison of the Multichannel Estimators of Coupling between Time Series 4.5.1. Bivariate versus Multivariate Connectivity Estimators 4.5.2. Linear versus Non-Linear Estimators of Connectivity 4.5.3. The Measures of Directedness 4.6. Multivariate Signal Decompositions 4.6.1. Principal Component Analysis (PCA) 4.6.1.1. Definition 4.6.1.2. Computation 4.6.1.3. Possible Applications 4.6.2. Independent Components Analysis (ICA) 4.6.2.1. Definition 4.6.2.2. Estimation of ICA 4.6.2.3. Computation 4.6.2.4. Possible Applications 4.6.3. Common Spatial Patterns 4.6.4. Multivariate Matching Pursuit (MMP) 5. Application to Biomedical Signals 5.1. Brain Signals 5.1.1. Generation of Brain Signals 5.1.2. EEG/MEG Rhythms 5.1.3. EEG Measurement, Electrode Systems 5.1.4. MEG Measurement, Sensor Systems 5.1.5. Elimination of Artifacts 5.1.6. Analysis of Continuous EEG Signals 5.1.6.1. Single Channel Analysis 5.1.6.2. Mapping 5.1.6.3. Connectivity Analysis of Brain Signals 5.1.6.4. In uence of Volume Conduction on Connectivity Measures 5.1.6.5. Graph Theoretical Analysis 5.1.6.6. Sleep EEG Analysis 5.1.6.7. Analysis of EEG in Epilepsy 5.1.6.8. EEG in Monitoring and Anesthesia 5.1.7. Analysis of Epoched EEG Signals 5.1.7.1. Analysis of Phase-Locked Responses 5.1.7.2. In Pursuit of Single Trial Evoked Responses 5.1.7.3. Applications of Cross-Frequency Coupling 5.1.7.4. Analysis of Non-Phase-Locked Responses 5.1.7.5. Analysis of EEG for Applications in Brain-Computer Interfaces 5.1.8. fMRI Derived Time Series 5.1.8.1. Relation between EEG and fMRI 5.1.9. Near-Infrared Spectroscopy Signals 5.2. Heart Signals 5.2.1. Electrocardiogram 5.2.1.1. Measurement Standards 5.2.1.2. Physiological Background and Clinical Applications 5.2.1.3. Processing of ECG 5.2.2. Heart Rate Variability 5.2.2.1. Time-Domain Methods of HRV Analysis 5.2.2.2. Frequency-Domain Methods of HRV Analysis 5.2.2.3. Non-Linear Methods of HRV Analysis 5.2.3. Fetal ECG 5.2.4. Magnetocardiogram and Fetal Magnetocardiogram 5.2.4.1. Magnetocardiogram 5.2.4.2. Fetal MCG 5.2.5. Ballistocardiogram, Seismocardiogram, Photoplethysmogram 5.2.5.1. Wearable Devices 5.3. Electromyogram 5.3.1. Measurement Techniques and Physiological Background 5.3.2. Quantification of EMG Features 5.3.3. Decomposition of Needle EMG 5.3.4. Surface EMG 5.3.4.1. Surface EMG Decomposition 5.4. Acoustic Signals 5.4.1. Phonocardiogram 5.4.2. Otoacoustic Emissions 5.5. Multimodal Analysis of Biomedical Signals Bibliography Index