Electromyography : physiology, engineering, and noninvasive applications

ntroduction xv Contributors xxi 1 BASIC PHYSIOLOGY AND BIOPHYSICS OF EMG SIGNAL GENERATION 1 T. Moritani, D. Stegeman, R. Merletti 1.1 Introduction 1 1.2 Basic Physiology of Motor Control and Muscle Contraction 2 1.2.1 Motor Unit 2 1.2.2 Motor Unit Recruitment and Firing Frequency (Rate Coding) 6 1.2.3 Factors Affecting Motor Unit Recruitment and Firing Frequency 9 1.2.4 Peripheral Motor Control System 11 1.2.5 Muscle Energetics and Neuromuscular Regulation 15 1.3 Basic Electrophysiology of the Muscle Cell Membrane 17 1.3.1 The Hodgkin-Huxley Model 17 1.3.2 Propagation of the Action Potential along the Muscle Fiber 19 References 20 2 NEEDLE AND WIRE DETECTION TECHNIQUES 27 J. V. Trontelj, J. Jabre, M. Mihelin 2.1 Anatomical and Physiological Background of Intramuscular Recording 27 2.2 Recording Characteristics of Needle Electrodes 29 2.3 Conventional Needle EMG 30 2.3.1 MUAP Parameters and Their Changes in Disease 32 2.3.2 Needle EMG at Increasing Voluntary Contraction 34 2.3.3 The Concentric Needle Electrode 34 2.3.4 The Monopolar Needle Electrode 35 2.4 Special Needle Recording Techniques 36 2.4.1 Single-Fiber EMG 36 2.4.2 Macro EMG 39 2.4.3 EMG Decomposition Technique with Quadrifilar Needle Electrode 41 2.4.4 Scanning EMG 41 2.5 Physical Characteristics of Needle EMG Signals 42 v 2.6 Recording Equipment 43 2.6.1 Principles of Instrumentation 43 2.6.2 Features of EMG Equipment 43 2.6.3 Features of Digitized Signals 45 2.6.4 Data Format 45 References 45 3 DECOMPOSITION OF INTRAMUSCULAR EMG SIGNALS 47 D. W. Stashuk, D. Farina, K. Sّgaard 3.1 Introduction 47 3.2 Basic Steps for EMG Signal Decomposition 48 3.2.1 EMG Signal Acquisition 49 3.2.2 Detecting MUAPs or Signal Segmentation 50 3.2.3 Feature Extraction for Pattern Recognition 52 3.2.4 Clustering of Detected MUAPs 53 3.2.5 Supervised Classification of Detected MUAPs 58 3.2.6 Resolving Superimposed MUAPs 63 3.2.7 Uncovering Temporal Relationships between MUAPTs 64 3.3 Evaluation of Performance of EMG Signal Decomposition Algorithms 67 3.3.1 Association between Reference and Detected MUs 67 3.3.2 Indexes of Performance 68 3.3.3 Evaluation of the Segmentation Phase Performance 68 3.3.4 Evaluation of the Classification Phase Performance 69 3.3.5 Reference Decomposition 70 3.4 Applications of Results of the Decomposition of an Intramuscular EMG Signal 70 3.4.1 Firing Pattern Analysis 71 3.4.2 Investigation of Correlation between MU Firing Patterns 74 3.4.3 Spike-Triggered Averaging of the Force Signal 75 3.4.4 Macro EMG 75 3.4.5 Spike-Triggered Averaging of the Surface EMG Signal 76 3.5 Conclusions 77 References 77 4 BIOPHYSICS OF THE GENERATION OF EMG SIGNALS 81 D. Farina, R. Merletti, D. F. Stegeman 4.1 Introduction 81 4.2 EMG Signal Generation 82 4.2.1 Signal Source 82 4.2.2 Generation and Extinction of the Intracellular Action Potential 85 4.2.3 Volume Conductor 87 4.2.4 EMG Detection, Electrode Montages and Electrode Size 89 vi CONTENTS 4.3 Crosstalk 91 4.3.1 Crosstalk Muscle Signals 91 4.3.2 Crosstalk and Detection System Selectivity 92 4.4 Relationships between Surface EMG Features and Developed Force 97 4.4.1 EMG Amplitude and Force 97 4.4.2 Estimated Conduction Velocity and Force 100 4.4.3 EMG Spectral Frequencies and Force 101 4.5 Conclusions 101 References 102 5 DETECTION AND CONDITIONING OF THE SURFACE EMG SIGNAL 107 R. Merletti, H. Hermens 5.1 Introduction 107 5.2 Electrodes: Their Transfer Function 108 5.3 Electrodes: Their Impedance, Noise, and dc Voltages 110 5.4 Electrode Configuration, Distance, Location 111 5.5 EMG Front-End Amplifiers 115 5.6 EMG Filters: Specifications 120 5.7 Sampling and A/D Conversion 121 5.8 European Recommendations on Electrodes and Electrode Locations 123 References 128 6 SINGLE-CHANNEL TECHNIQUES FOR INFORMATION EXTRACTION FROM THE SURFACE EMG SIGNAL 133 E. A. Clancy, D. Farina, G. Filligoi 6.1 Introduction 133 6.2 Spectral Estimation of Deterministic Signals and Stochastic Processes 134 6.2.1 Fourier-Based Spectral Estimators 134 6.2.2 Parametric Based Spectral Estimators 135 6.2.3 Estimation of the Time-Varying PSD of Nonstationary Stochastic Processes 137 6.3 Basic Surface EMG Signal Models 137 6.4 Surface EMG Amplitude Estimation 139 6.4.1 Measures of Amplitude Estimator Performance 141 6.4.2 EMG Amplitude Processing—Overview 141 6.4.3 Applications of EMG Amplitude Estimation 145 6.5 Extraction of Information in Frequency Domain from Surface EMG Signals 145 6.5.1 Estimation of PSD of the Surface EMG Signal Detected during Voluntary Contractions 146 CONTENTS vii 6.5.2 Energy Spectral Density of the Surface EMG Signal Detected during Electrically Elicited Contractions 148 6.5.3 Descriptors of Spectral Compression 148 6.5.4 Other Approaches for Detecting Changes in Surface EMG Frequency Content during Voluntary Contractions 152 6.5.5 Applications of Spectral Analysis of the Surface EMG Signal 153 6.6 Joint Analysis of EMG Spectrum and Amplitude (JASA) 153 6.7 Recurrence Quantification Analysis of Surface EMG Signals 154 6.7.1 Mathematical Bases of RQA 155 6.7.2 Main Features of RQA 159 6.7.3 Application of RQA to Analysis of Surface EMG Signals 159 6.8 Conclusions 162 References 163 7 MULTI-CHANNEL TECHNIQUES FOR INFORMATION EXTRACTION FROM THE SURFACE EMG 169 D. Farina, R. Merletti, C. Disselhorst-Klug 7.1 Introduction 169 7.2 Spatial Filtering 170 7.2.1 Idea Underlying Spatial Filtering 170 7.2.2 Mathematical Basis for the Description of Spatial Filters Comprised of Point Electrodes 173 7.2.3 Two-Dimensional Spatial Filters Comprised of Point Electrodes 174 7.2.4 Spatial Filters Comprised of Nonpoint Electrodes 177 7.2.5 Applications of Spatial Filtering Techniques 179 7.2.6 A Note on Crosstalk 180 7.3 Spatial Sampling 180 7.3.1 Linear Electrode Arrays 181 7.3.2 Two-Dimensional Spatial Sampling 183 7.4 Estimation of Muscle-Fiber Conduction Velocity 185 7.4.1 Two Channel-Based Methods for CV Estimation 186 7.4.2 Methods for CV Estimation Based on More Than Two Channels 190 7.4.3 Single MU CV Estimation 191 7.4.4 Influence of Anatomical, Physical, and Detection System Parameters on CV Estimates 196 7.5 Conclusions 196 References 199 8 EMG MODELING AND SIMULATION 205 D. F. Stegeman, R. Merletti, H. J. Hermens 8.1 Introduction 205 8.2 Phenomenological Models of EMG 207 viii CONTENTS 8.3 Elements of Structure-Based SEMG Models 207 8.4 Basic Assumptions 209 8.5 Elementary Sources of Bioelectric Muscle Activity 209 8.5.1 The Lowest Level: Intracellular Muscle-Fiber Action Potentials 209 8.5.2 The Highest Level: MU Action Potentials 210 8.6 Fiber Membrane Activity Profiles, Their Generation, Propagation, and Extinction 210 8.7 Structure of the Motor Unit 213 8.7.1 General Considerations 213 8.7.2 Inclusion of Force in Motor Unit Modeling 213 8.8 Volume Conduction 214 8.8.1 General Considerations 214 8.8.2 Basics Concepts 215 8.9 Modeling EMG Detection Systems 215 8.9.1 Electrode Configuration 216 8.9.2 Physical Dimensions of the Electrodes 216 8.10 Modeling Motor Unit Recruitment and Firing Behavior 218 8.10.1 MU Interpulse Intervals 220 8.10.2 Mean Interpulse Intervals across Motor Units 220 8.10.3 Synchronization 220 8.11 Inverse Modeling 222 8.12 Modeling of Muscle Fatigue 222 8.12.1 Myoelectric Manifestations of Muscle Fatigue during Voluntary Contractions 222 8.12.2 Myoelectric Manifestations of Muscle Fatigue during Electrically Elicited Contractions 224 8.13 Other Applications of Modeling 226 8.14 Conclusions 227 References 227 9 MYOELECTRIC MANIFESTATIONS OF MUSCLE FATIGUE 233 R. Merletti, A. Rainoldi, D. Farina 9.1 Introduction 233 9.2 Definitions and Sites of Neuromuscular Fatigue 234 9.3 Assessment of Muscle Fatigue 235 9.4 How Fatigue Is Reflected in Surface EMG Variables 236 9.5 Myoelectric Manifestations of Muscle Fatigue in Isometric Voluntary Contractions 238 9.6 Fiber Typing and Myoelectric Manifestations of Muscle Fatigue 242 9.7 Factors Affecting Surface EMG Variables 246 9.7.1 Isometric Contractions 246 9.7.2 Dynamic Contractions 251 CONTENTS ix 9.8 Repeatability of Estimates of EMG Variables and Fatigue Indexes 251 9.9 Conclusions 252 References 253 10 ADVANCED SIGNAL PROCESSING TECHNIQUES 259 D. Zazula, S. Karlsson, C. Doncarli 10.1 Introduction 259 10.1.1 Parametric Context 260 10.1.2 Nonparametric Context 260 10.1.3 Conclusion 261 10.2 Theoretical Background 261 10.2.1 Multichannel Models of Compound Signals 261 10.2.2 Stochastic Processes 264 10.2.3 Time-Frequency Representations 269 10.2.4 Wavelet Transform 272 10.2.5 Improving the PSD Estimation Using Wavelet Shrinkage 279 10.2.6 Spectral Shape Indicators 280 10.3 Decomposition of EMG Signals 281 10.3.1 Parametric Decomposition of EMG Signals Using Wavelet Transform 281 10.3.2 Decomposition of EMG Signal Using Higher Order Statistics 287 10.4 Applications to Monitoring Myoelectric Manifestations of Muscle Fatigue 292 10.4.1 Myoelectric Manifestations of Muscle Fatigue during Static Contractions 293 10.4.2 Myoelectric Manifestations of Muscle Fatigue during Dynamic Contraction 296 10.5 Conclusions 300 Acknowledgment 302 References 302 11 SURFACE MECHANOMYOGRAM 305 C. Orizio 11.1 The Mechanomyogram (MMG): General Aspects during Stimulated and Voluntary Contraction 305 11.2 Detection Techniques and Sensors Comparison 307 11.2.1 MMG Detected by Laser Distance Sensors 307 11.2.2 MMG Detected by Accelerometers 308 11.2.3 MMG Detected by Piezoelectric Contact Sensors 309 11.2.4 MMG Detected by Microphones 310 11.3 Comparison between Different Detectors 310 11.4 Simulation 312 11.5 MMG Versus Force: Joint and Adjunct Information Content 313 x CONTENTS 11.6 MMG Versus EMG: Joint and Adjunct Information Content 316 11.7 Area of Application 318 References 318 12 SURFACE EMG APPLICATIONS IN NEUROLOGY 323 M. J. Zwarts, D. F. Stegeman, J. G. van Dijk 12.1 Introduction 323 12.2 Central Nervous System Disorders and SEMG 324 12.3 Compound Muscle Action Potential and Motor Nerve Conduction 326 12.4 CMAP Generation 328 12.4.1 CMAP as a Giant MUAP 328 12.4.2 Muscle Cartography 330 12.5 Clinical Applications 332 12.5.1 Amplitude: What Does It Stand For? 332 12.5.2 Deriving Conduction Properties from Two CMAPs 333 12.6 Pathological Fatigue 335 12.7 New Avenues: High-Density Multichannel Recording 338 12.8 Conclusion 341 References 341 13 APPLICATIONS IN ERGONOMICS 343 G. M. Hنgg, B. Melin, R. Kadefors 13.1 Historic Perspective 343 13.2 Basic Workload Concepts in Ergonomics 344 13.3 Basic Surface EMG Signal Processing 345 13.4 Load Estimation and SEMG Normalization and Calibration 346 13.5 Amplitude Data Reduction over Time 347 13.6 Electromyographic Signal Alterations Indicating Muscle Fatigue in Ergonomics 348 13.7 SEMG Biofeedback in Ergonomics 352 13.8 Surface EMG and Musculoskeletal Disorders 352 13.9 Psychological Effects on EMG 353 13.9.1 Definitions of Stress 354 13.9.2 Psychological and Physical Stress and the Total Workload on the Organism 354 13.9.3 Psychological Stress and Musculoskeletal Disorders 355 13.9.4 Two Neuroendocrine Systems Sensitive to Psychological Stress 355 13.9.5 Is It Justified to Include EMG in the Field of Stress? 355 13.9.6 Mental Stress Increases EMG Activity 356 13.9.7 Is the Trapezius Muscle Special in Its Response to Psychological Stress? 356 CONTENTS xi 13.9.8 Psychological Factors and Possible Links to Musculoskeletal Tension 357 13.9.9 Conclusions 358 References 358 14 APPLICATIONS IN EXERCISE PHYSIOLOGY 365 F. Felici 14.1 Introduction 365 14.2 A Few “Tips and Tricks” 366 14.3 Time and Frequency Domain Analysis of sEMG: What Are We Looking For? 368 14.4 Application of sEMG to the Study of Exercise 370 14.4.1 Walking versus Race Walking and Running 370 14.4.2 Gait Analysis Results 371 14.5 Strength and Power Training 372 14.6 Muscle Damage Studied by Means of sEMG 375 References 377 15 APPLICATIONS IN MOVEMENT AND GAIT ANALYSIS 381 C. Frigo, R. Shiavi 15.1 Relevance of Electromyography in Kinesiology 381 15.2 Typical Acquisition Settings 382 15.3 Study of Motor Control Strategies 384 15.4 Investigation on the Mechanical Effect of Muscle Contraction 385 15.5 Gait Analysis 386 15.6 Identification of Pathophysiologic Factors 387 15.7 Workload Assessment in Occupational Biomechanics 388 15.8 Biofeedback 389 15.9 The Linear Envelope 389 15.9.1 Construction of the Linear Envelope 390 15.9.2 EMG Profiles 390 15.9.3 Repeatability 391 15.10 Information Enhancement through Multifactorial Analysis 393 15.10.1 Measured Variables 393 15.10.2 Measured and Derived Variables 397 References 397 16 APPLICATIONS IN REHABILITATION MEDICINE AND RELATED FIELDS 403 A. Rainoldi, R. Casale, P. Hodges, G. Jull 16.1 Introduction 403 16.2 Electromyography as a Tool in Back and Neck Pain 404 xii CONTENTS 16.2.1 Electromyography as a Tool to Investigate Motor Control of the Spine 404 16.2.2 Application to Neck Pain 409 16.2.3 Analysis in the Frequency Domain 410 16.3 EMG of the Pelvic Floor: A New Challenge in Neurological Rehabilitation 411 16.3.1 Introduction 411 16.3.2 Anatomy of the Pelvic Floor 412 16.3.3 Physiopathology of the Pelvic Floor 412 16.3.4 Routine Evaluation of the Pelvic Floor 412 16.4 Age-Related Effects on EMG Assessment of Muscle Physiology 417 16.4.1 Muscle Strength 417 16.4.2 Fiber Type Composition 418 16.4.3 Myoelectrical Manifestation of Muscle Fatigue 419 16.5 Surface EMG and Hypobaric Hipoxia 420 16.5.1 Physiological Modification Induced by Hypoxia 421 16.5.2 Modification of Mechanical Muscle Response Induced by Hypoxia 421 16.5.3 Modification of Fiber Type Induced by Hypoxia 421 16.5.4 Modification of Muscle Fatigue Induced by Hypoxia 421 16.5.5 The Role of Acclimatization 422 16.6 Microgravity Effects on Neuromuscular System 423 16.6.1 Postflight Effects on Humans 423 16.6.2 Postflight Effects on Animals 423 16.6.3 Models of Microgravity Effects 424 16.6.4 Microgravity Effect, Duration, and Countermeasures 425 References 425 17 BIOFEEDBACK APPLICATIONS 435 J. R. Cram 17.1 Introduction 435 17.2 Biofeedback Application to Impairment Syndromes 436 17.2.1 Psychophysiological, Stress-Related Hyperactivity 436 17.2.2 Simple Postural Dysfunction 437 17.2.3 Weakness/Deconditioning 438 17.2.4 Acute Reflexive Spasm/Inhibition 439 17.2.5 Learned Guarding/Bracing 439 17.2.6 Learned Inhibition/Weakness 440 17.2.7 Direct Compensation for Joint Hypermobility or Hypomobility 441 17.2.8 Chronic Faulty Motor Programs 442 17.3 SEMG Biofeedback Techniques 443 17.3.1 Isolation of Target Muscle Activity 443 CONTENTS xiii 17.3.2 Relaxation-Based Downtraining 444 17.3.3 Threshold-Based Uptraining or Downtraining 445 17.3.4 Threshold-Based Tension Recognition Training 445 17.3.5 Tension Discrimination Training 446 17.3.6 Deactivation Training 446 17.3.7 Generalization to Progressively Dynamic Movement 446 17.3.8 SEMG-Triggered Neuromuscular Electrical Stimulation (NMES) 448 17.3.9 Left/Right Equilibration Training 448 17.3.10 Motor Copy Training 449 17.3.11 Postural Training with SEMG Feedback 449 17.3.12 Body Mechanics Instruction 449 17.3.13 Therapeutic Exercise with SEMG Feedback 449 17.3.14 Functional Activity Performance with SEMG Feedback 450 17.4 Summary 450 References 450 18 CONTROL OF POWERED UPPER LIMB PROSTHESES 453 P. A. Parker, K. B. Englehart, B. S. Hudgins 18.1 Introduction 453 18.2 Myoelectric Signal as a Control Input 455 18.2.1 Single Myoelectric Channel Model 455 18.2.2 Single-Channel Control Information 457 18.2.3 Limitations of the Single-Channel Myoelectric Signal as Control Input 458 18.2.4 Multiple Myoelectric Channels 460 18.3 Conventional Myoelectric Control 460 18.4 Emerging MEC Strategies 463 18.4.1 Pattern Recognition Based Control 463 18.4.2 Intelligent Subsystems 468 18.5 Summary 471 References 471 Index 477