Electromyography : physiology, engineering, and noninvasive applications /

Electromyography : physiology, engineering, and noninvasive applications /

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Bibliographic Details
Imprint:Hoboken, NJ : Wiley-Interscience ; Piscataway, NJ : IEEE Press, c2004.
Description:1 online resource (xxii, 494 p.) : ill.
Language:English
Series:IEEE Press series in biomedical engineering
IEEE Press series in biomedical engineering.
Subject:
Electromyography.
Muscles.
Neuromuscular diseases -- Diagnosis.
Electromyography.
Muscles.
Neuromuscular diseases -- Diagnosis.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8680115
Hidden Bibliographic Details
Other authors / contributors:Merletti, Roberto.
Parker, Philip (Philip A.)
John Wiley & Sons.
ISBN:0471675806 (print. ed.)
9780471675808 (print. ed.)
0471678384 (electronic bk.)
9780471678380 (electronic bk.)
Notes:Includes bibliographical references and index.
Summary:"Featuring contributions from key innovators working in the field today, Electromyography reveals the broad applications of EMG data in areas as diverse as neurology, ergonomics, exercise physiology, rehabilitation, movement analysis, biofeedback, and myoelectric control of prostheses." "Electromyography offers physiologists, medical professionals, and students in biomedical engineering a new window into the possibilities of this technology."--Jacket.
Other form:Print version: Electromyography. Hoboken, NJ : Wiley-Interscience ; Piscataway, NJ : IEEE Press, c2004 0471675806
Standard no.:10.1002/0471678384
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

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