Electromyography : physiology, engineering, and noninvasive applications /

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Bibliographic Details
Imprint:	Piscataway, NJ : IEEE Press ; Hoboken, N.J. : Wiley-Interscience, c2004.
Description:	xxii, 494 p. : ill. ; 26 cm.
Language:	English
Series:	IEEE Press series in biomedical engineering
Subject:	Electromyography. Muscles. Neuromuscular Diseases -- diagnosis. Electromyography. Muscles. Neuromuscular diseases -- Diagnosis.
Format:	Print Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/5276338

Hidden Bibliographic Details
Other authors / contributors:	Merletti, Roberto. Parker, Philip (Philip A.)
ISBN:	0471675806
Notes:	Includes bibliographical references and index.

Table of Contents:

Introduction
Contributors
1. Basic Physiology and Biophysics of EMG Signal Generation
1.1. Introduction
1.2. Basic Physiology of Motor Control and Muscle Contraction
1.2.1. Motor Unit
1.2.2. Motor Unit Recruitment and Firing Frequency (Rate Coding)
1.2.3. Factors Affecting Motor Unit Recruitment and Firing Frequency
1.2.4. Peripheral Motor Control System
1.2.5. Muscle Energetics and Neuromuscular Regulation
1.3. Basic Electrophysiology of the Muscle Cell Membrane
1.3.1. The Hodgkin-Huxley Model
1.3.2. Propagation of the Action Potential along the Muscle Fiber
References
2. Needle and Wire Detection Techniques
2.1. Anatomical and Physiological Background of Intramuscular Recording
2.2. Recording Characteristics of Needle Electrodes
2.3. Conventional Needle EMG
2.3.1. MUAP Parameters and Their Changes in Disease
2.3.2. Needle EMG at Increasing Voluntary Contraction
2.3.3. The Concentric Needle Electrode
2.3.4. The Monopolar Needle Electrode
2.4. Special Needle Recording Techniques
2.4.1. Single-Fiber EMG
2.4.2. Macro EMG
2.4.3. EMG Decomposition Technique with Quadrifilar Needle Electrode
2.4.4. Scanning EMG
2.5. Physical Characteristics of Needle EMG Signals
2.6. Recording Equipment
2.6.1. Principles of Instrumentation
2.6.2. Features of EMG Equipment
2.6.3. Features of Digitized Signals
2.6.4. Data Format
References
3. Decomposition of Intramuscular EMG Signals
3.1. Introduction
3.2. Basic Steps for EMG Signal Decomposition
3.2.1. EMG Signal Acquisition
3.2.2. Detecting MUAPs or Signal Segmentation
3.2.3. Feature Extraction for Pattern Recognition
3.2.4. Clustering of Detected MUAPs
3.2.5. Supervised Classification of Detected MUAPs
3.2.6. Resolving Superimposed MUAPs
3.2.7. Uncovering Temporal Relationships between MUAPTs
3.3. Evaluation of Performance of EMG Signal Decomposition Algorithms
3.3.1. Association between Reference and Detected MUs
3.3.2. Indexes of Performance
3.3.3. Evaluation of the Segmentation Phase Performance
3.3.4. Evaluation of the Classification Phase Performance
3.3.5. Reference Decomposition
3.4. Applications of Results of the Decomposition of an Intramuscular EMG Signal
3.4.1. Firing Pattern Analysis
3.4.2. Investigation of Correlation between MU Firing Patterns
3.4.3. Spike-Triggered Averaging of the Force Signal
3.4.4. Macro EMG
3.4.5. Spike-Triggered Averaging of the Surface EMG Signal
3.5. Conclusions
References
4. Biophysics of the Generation of EMG Signals
4.1. Introduction
4.2. EMG Signal Generation
4.2.1. Signal Source
4.2.2. Generation and Extinction of the Intracellular Action Potential
4.2.3. Volume Conductor
4.2.4. EMG Detection, Electrode Montages and Electrode Size
4.3. Crosstalk
4.3.1. Crosstalk Muscle Signals
4.3.2. Crosstalk and Detection System Selectivity
4.4. Relationships between Surface EMG Features and Developed Force
4.4.1. EMG Amplitude and Force
4.4.2. Estimated Conduction Velocity and Force
4.4.3. EMG Spectral Frequencies and Force
4.5. Conclusions
References
5. Detection and Conditioning of the Surface EMG Signal
5.1. Introduction
5.2. Electrodes: Their Transfer Function
5.3. Electrodes: Their Impedance, Noise, and dc Voltages
5.4. Electrode Configuration, Distance, Location
5.5. EMG Front-End Amplifiers
5.6. EMG Filters: Specifications
5.7. Sampling and A/D Conversion
5.8. European Recommendations on Electrodes and Electrode Locations
References
6. Single-Channel Techniques for Information Extraction From the Surface Emg Signal
6.1. Introduction
6.2. Spectral Estimation of Deterministic Signals and Stochastic Processes
6.2.1. Fourier-Based Spectral Estimators
6.2.2. Parametric Based Spectral Estimators
6.2.3. Estimation of the Time-Varying PSD of Nonstationary Stochastic Processes
6.3. Basic Surface EMG Signal Models
6.4. Surface EMG Amplitude Estimation
6.4.1. Measures of Amplitude Estimator Performance
6.4.2. EMG Amplitude Processing--Overview
6.4.3. Applications of EMG Amplitude Estimation
6.5. Extraction of Information in Frequency Domain from Surface EMG Signals
6.5.1. Estimation of PSD of the Surface EMG Signal Detected during Voluntary Contractions
6.5.2. Energy Spectral Density of the Surface EMG Signal Detected during Electrically Elicited Contractions
6.5.3. Descriptors of Spectral Compression
6.5.4. Other Approaches for Detecting Changes in Surface EMG Frequency Content during Voluntary Contractions
6.5.5. Applications of Spectral Analysis of the Surface EMG Signal
6.6. Joint Analysis of EMG Spectrum and Amplitude (JASA)
6.7. Recurrence Quantification Analysis of Surface EMG Signals
6.7.1. Mathematical Bases of RQA
6.7.2. Main Features of RQA
6.7.3. Application of RQA to Analysis of Surface EMG Signals
6.8. Conclusions
References
7. Multi-Channel Techniques for Information Extraction from the Surface EMG
7.1. Introduction
7.2. Spatial Filtering
7.2.1. Idea Underlying Spatial Filtering
7.2.2. Mathematical Basis for the Description of Spatial Filters Comprised of Point Electrodes
7.2.3. Two-Dimensional Spatial Filters Comprised of Point Electrodes
7.2.4. Spatial Filters Comprised of Nonpoint Electrodes
7.2.5. Applications of Spatial Filtering Techniques
7.2.6. A Note on Crosstalk
7.3. Spatial Sampling
7.3.1. Linear Electrode Arrays
7.3.2. Two-Dimensional Spatial Sampling
7.4. Estimation of Muscle-Fiber Conduction Velocity
7.4.1. Two Channel-Based Methods for CV Estimation
7.4.2. Methods for CV Estimation Based on More Than Two Channels
7.4.3. Single MU CV Estimation
7.4.4. Influence of Anatomical, Physical, and Detection System Parameters on CV Estimates
7.5. Conclusions
References
8. EMG Modeling and Simulation
8.1. Introduction
8.2. Phenomenological Models of EMG
8.3. Elements of Structure-Based SEMG Models
8.4. Basic Assumptions
8.5. Elementary Sources of Bioelectric Muscle Activity
8.5.1. The Lowest Level: Intracellular Muscle-Fiber Action Potentials
8.5.2. The Highest Level: MU Action Potentials
8.6. Fiber Membrane Activity Profiles, Their Generation, Propagation, and Extinction
8.7. Structure of the Motor Unit
8.7.1. General Considerations
8.7.2. Inclusion of Force in Motor Unit Modeling
8.8. Volume Conduction
8.8.1. General Considerations
8.8.2. Basics Concepts
8.9. Modeling EMG Detection Systems
8.9.1. Electrode Configuration
8.9.2. Physical Dimensions of the Electrodes
8.10. Modeling Motor Unit Recruitment and Firing Behavior
8.10.1. MU Interpulse Intervals
8.10.2. Mean Interpulse Intervals across Motor Units
8.10.3. Synchronization
8.11. Inverse Modeling
8.12. Modeling of Muscle Fatigue
8.12.1. Myoelectric Manifestations of Muscle Fatigue during Voluntary Contractions
8.12.2. Myoelectric Manifestations of Muscle Fatigue during Electrically Elicited Contractions
8.13. Other Applications of Modeling
8.14. Conclusions
References
9. Myoelectric Manifestations of Muscle Fatigue
9.1. Introduction
9.2. Definitions and Sites of Neuromuscular Fatigue
9.3. Assessment of Muscle Fatigue
9.4. How Fatigue Is Reflected in Surface EMG Variables
9.5. Myoelectric Manifestations of Muscle Fatigue in Isometric Voluntary Contractions
9.6. Fiber Typing and Myoelectric Manifestations of Muscle Fatigue
9.7. Factors Affecting Surface EMG Variables
9.7.1. Isometric Contractions
9.7.2. Dynamic Contractions
9.8. Repeatability of Estimates of EMG Variables and Fatigue Indexes
9.9. Conclusions
References
10. Advanced Signal Processing Techniques
10.1. Introduction
10.1.1. Parametric Context
10.1.2. Nonparametric Context
10.1.3. Conclusion
10.2. Theoretical Background
10.2.1. Multichannel Models of Compound Signals
10.2.2. Stochastic Processes
10.2.3. Time-Frequency Representations
10.2.4. Wavelet Transform
10.2.5. Improving the PSD Estimation Using Wavelet Shrinkage
10.2.6. Spectral Shape Indicators
10.3. Decomposition of EMG Signals
10.3.1. Parametric Decomposition of EMG Signals Using Wavelet Transform
10.3.2. Decomposition of EMG Signal Using Higher Order Statistics
10.4. Applications to Monitoring Myoelectric Manifestations of Muscle Fatigue
10.4.1. Myoelectric Manifestations of Muscle Fatigue during Static Contractions
10.4.2. Myoelectric Manifestations of Muscle Fatigue during Dynamic Contraction
10.5. Conclusions
Acknowledgment
References
11. Surface Mechanomyogram
11.1. The Mechanomyogram (MMG): General Aspects during Stimulated and Voluntary Contraction
11.2. Detection Techniques and Sensors Comparison
11.2.1. MMG Detected by Laser Distance Sensors
11.2.2. MMG Detected by Accelerometers
11.2.3. MMG Detected by Piezoelectric Contact Sensors
11.2.4. MMG Detected by Microphones
11.3. Comparison between Different Detectors
11.4. Simulation
11.5. MMG Versus Force: Joint and Adjunct Information Content
11.6. MMG Versus EMG: Joint and Adjunct Information Content
11.7. Area of Application
References
12. Surface EMG Applications in Neurology
12.1. Introduction
12.2. Central Nervous System Disorders and SEMG
12.3. Compound Muscle Action Potential and Motor Nerve Conduction
12.4. CMAP Generation
12.4.1. CMAP as a Giant MUAP
12.4.2. Muscle Cartography
12.5. Clinical Applications
12.5.1. Amplitude: What Does It Stand For?
12.5.2. Deriving Conduction Properties from Two CMAPs
12.6. Pathological Fatigue
12.7. New Avenues: High-Density Multichannel Recording
12.8. Conclusion
References
13. Applications in Ergonomics
13.1. Historic Perspective
13.2. Basic Workload Concepts in Ergonomics
13.3. Basic Surface EMG Signal Processing
13.4. Load Estimation and SEMG Normalization and Calibration
13.5. Amplitude Data Reduction over Time
13.6. Electromyographic Signal Alterations Indicating Muscle Fatigue in Ergonomics
13.7. SEMG Biofeedback in Ergonomics
13.8. Surface EMG and Musculoskeletal Disorders
13.9. Psychological Effects on EMG
13.9.1. Definitions of Stress
13.9.2. Psychological and Physical Stress and the Total Workload on the Organism
13.9.3. Psychological Stress and Musculoskeletal Disorders
13.9.4. Two Neuroendocrine Systems Sensitive to Psychological Stress
13.9.5. Is It Justified to Include EMG in the Field of Stress?
13.9.6. Mental Stress Increases EMG Activity
13.9.7. Is the Trapezius Muscle Special in Its Response to Psychological Stress?
13.9.8. Psychological Factors and Possible Links to Musculoskeletal Tension
13.9.9. Conclusions
References
14. Applications in Exercise Physiology
14.1. Introduction
14.2. A Few "Tips and Tricks"
14.3. Time and Frequency Domain Analysis of sEMG: What Are We Looking For?
14.4. Application of sEMG to the Study of Exercise
14.4.1. Walking versus Race Walking and Running
14.4.2. Gait Analysis Results
14.5. Strength and Power Training
14.6. Muscle Damage Studied by Means of sEMG
References
15. Applications in Movement and Gait Analysis
15.1. Relevance of Electromyography in Kinesiology
15.2. Typical Acquisition Settings
15.3. Study of Motor Control Strategies
15.4. Investigation on the Mechanical Effect of Muscle Contraction
15.5. Gait Analysis
15.6. Identification of Pathophysiologic Factors
15.7. Workload Assessment in Occupational Biomechanics
15.8. Biofeedback
15.9. The Linear Envelope
15.9.1. Construction of the Linear Envelope
15.9.2. EMG Profiles
15.9.3. Repeatability
15.10. Information Enhancement through Multifactorial Analysis
15.10.1. Measured Variables
15.10.2. Measured and Derived Variables
References
16. Applications in Rehabilitation Medicine and Related Fields
16.1. Introduction
16.2. Electromyography as a Tool in Back and Neck Pain
16.2.1. Electromyography as a Tool to Investigate Motor Control of the Spine
16.2.2. Application to Neck Pain
16.2.3. Analysis in the Frequency Domain
16.3. EMG of the Pelvic Floor: A New Challenge in Neurological Rehabilitation
16.3.1. Introduction
16.3.2. Anatomy of the Pelvic Floor
16.3.3. Physiopathology of the Pelvic Floor
16.3.4. Routine Evaluation of the Pelvic Floor
16.4. Age-Related Effects on EMG Assessment of Muscle Physiology
16.4.1. Muscle Strength
16.4.2. Fiber Type Composition
16.4.3. Myoelectrical Manifestation of Muscle Fatigue
16.5. Surface EMG and Hypobaric Hipoxia
16.5.1. Physiological Modification Induced by Hypoxia
16.5.2. Modification of Mechanical Muscle Response Induced by Hypoxia
16.5.3. Modification of Fiber Type Induced by Hypoxia
16.5.4. Modification of Muscle Fatigue Induced by Hypoxia
16.5.5. The Role of Acclimatization
16.6. Microgravity Effects on Neuromuscular System
16.6.1. Postflight Effects on Humans
16.6.2. Postflight Effects on Animals
16.6.3. Models of Microgravity Effects
16.6.4. Microgravity Effect, Duration, and Countermeasures
References
17. Biofeedback Applications
17.1. Introduction
17.2. Biofeedback Application to Impairment Syndromes
17.2.1. Psychophysiological, Stress-Related Hyperactivity
17.2.2. Simple Postural Dysfunction
17.2.3. Weakness/Deconditioning
17.2.4. Acute Reflexive Spasm/Inhibition
17.2.5. Learned Guarding/Bracing
17.2.6. Learned Inhibition/Weakness
17.2.7. Direct Compensation for Joint Hypermobility or Hypomobility
17.2.8. Chronic Faulty Motor Programs
17.3. SEMG Biofeedback Techniques
17.3.1. Isolation of Target Muscle Activity
17.3.2. Relaxation-Based Downtraining
17.3.3. Threshold-Based Uptraining or Downtraining
17.3.4. Threshold-Based Tension Recognition Training
17.3.5. Tension Discrimination Training
17.3.6. Deactivation Training
17.3.7. Generalization to Progressively Dynamic Movement
17.3.8. SEMG-Triggered Neuromuscular Electrical Stimulation (NMES)
17.3.9. Left/Right Equilibration Training
17.3.10. Motor Copy Training
17.3.11. Postural Training with SEMG Feedback
17.3.12. Body Mechanics Instruction
17.3.13. Therapeutic Exercise with SEMG Feedback
17.3.14. Functional Activity Performance with SEMG Feedback
17.4. Summary
References
18. Control of Powered Upper Limb Prostheses
18.1. Introduction
18.2. Myoelectric Signal as a Control Input
18.2.1. Single Myoelectric Channel Model
18.2.2. Single-Channel Control Information
18.2.3. Limitations of the Single-Channel Myoelectric Signal as Control Input
18.2.4. Multiple Myoelectric Channels
18.3. Conventional Myoelectric Control
18.4. Emerging MEC Strategies
18.4.1. Pattern Recognition Based Control
18.4.2. Intelligent Subsystems
18.5. Summary
References
Index

Electromyography : physiology, engineering, and noninvasive applications /

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