Electromyography: Processing

1
Electromyography Processing

• D. Gordon E. Robertson, PhD, FCSB
• Biomechanics Laboratory,
• School of Human Kinetics,
• University of Ottawa, Ottawa, Canada

2
Types of Signal Processing

• Raw (with or without band-pass filtering)
• Full-wave rectified (absolute value)
• Averaged or root-mean-square (RMS)
• Linear envelope
• Ensemble-averaged
• Integrated EMG (iEMG)
• Frequency or power spectrum (Fourier)
• Fatigue analysis (sequential Fourier)
• Amplitude probability distribution function
  (APDF) and CAPDF
• Conduction velocity
• Wavelet transform

3
Raw EMG

• wide frequency spectrum (20-500 Hz)
• most complete information
• needs 1000 Hz or greater sampling rates
• requires large memory storage
• difficult to determine levels of contraction
• bursts of activity and onset times may be
  determined from this signal
• best for examining problems with recording
• following slides show some errors that can be
  detected from the raw signal

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Errors when Recording EMGs

• clean signal
• ECG crosstalk

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ECG Crosstalk

• ECG crosstalk occurs when recording near the
  heart (ECG has higher voltages then EMG)
• EEG crosstalk when near scalp (rare)
• difficult to resolve
• use right side of body (away from heart)
• move electrodes as far away from heart as
  possible
• signal averaging (average many trials)
• indwelling electrodes

6
Muscle Crosstalk

• one muscles EMG is picked up by another muscles
  electrodes
• can be reduced by careful electrode positioning
  or double differential amplifier
• can be determined by cross-correlation
• difficult to distinguish crosstalk from
  synergistic contractions, however, biarticular
  muscles have extra bursts of activity compared
  to monoarticular muscles (thus crosstalk is not a
  problem)

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Errors when Recording EMGs

• line (AC) interference
• DC-offset or DC-bias

8
Solutions

• To interference (line AC and radio frequency RF
  etc.)
• Keep away from fluorescent lighting
• Keep away from large electrical devices and power
  cords (especially leads and cabling)
• Use room lined with grounded conductive material
• Keep leads short and braided (vs. radio)
• Use preamplified electrodes (signal is stronger)
• Use extremely narrow notch filter in post
  processing (e.g., 59.5-60.5 Hz)
• For DC-offsets (telemetry systems often have
  DC-offsets)
• Use a good ground electrode over electrically
  neutral area
• Use high-pass filter (510 Hz) to remove in
  post-processing

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Errors when Recording EMGs

• movement artifact
• amplifier saturation (/0.5 V)

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Solutions

• To movement artifacts
• Affix leads to subject (tape, wrap, webbing)
• Prevent electrodes from being struck (use lateral
  muscles)
• Avoid rapid motions
• Use strong high-pass filter in post-processing
• Amplifier saturation
• Test with maximal contractions before recording
• Reduce gain if peaks and valleys top out or
  bottom out
• Use larger range A/D converter (/10 V vs. /5
  V)

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Full-wave Rectified EMG

• same as taking the absolute value of the raw
  signal
• mainly used as an intermediate step before
  another process (e.g., averaging, linear
  envelope, and integration)
• can be done electronically in real-time

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Sample EMGs

• raw EMG (band-passed filtered, 20-500 Hz)
• full-wave rectified

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Averaged EMG

• simple to compute
• can be done in real-time
• averaged EMG is a moving average of a
  full-wave rectified EMG
• must select an appropriate window width that
  changes with sampling rate
• easy for determining levels of contraction

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Sample Averaged EMG

• raw EMG (1010 Hz sampling rate)
• averaged EMG (moving average, 51 points)

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Linear Envelope EMG

• requires two-step process full-wave
  rectification followed by low-pass filter (4-10
  Hz cutoff)
• can be done electronically (but adds a delay)
• reduces frequency content of EMG and thus lowers
  sampling rates (e.g., 100 Hz) and memory storage
• easy to interpret and to detect onset of activity
• can be ensemble-averaged to obtain patterns
• difficult to detect artifacts
• useful as a control (myoelectric) signal

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Sample LE-EMG

• raw (band-passed filtered) EMG
• linear envelope EMG (cutoff 4 Hz)

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Ensemble-Averaged EMG

• usually applied to cyclic activities and linear
  envelope EMGs
• requires method for identifying start of a cycle
  or start and end of an activity
• foot switches or force platforms can be used for
  gait studies
• microswitches, optoelectric, or electromagnetic
  sensors for other activities
• can also use a threshold detector of a kinematic
  or EMG channel
• each cycle of activity must be time normalized

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Ensemble-Averaged EMG contd

• amplitude normalization is often done
• to maximal voluntary contraction (MVC)
• to submaximal isometric contraction
• to EMG of a functional activity (e.g., holding a
  load)
• mean and standard deviations for each proportion
  of cycle are computed
• mean and s.d. or 95 confidence interval may be
  presented to show representative contraction
  during activity cycle
• easier to make comparisons among subjects
• grand ensemble-averages (average of averages)
  for comparisons among several experimental
  conditions

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Ensemble-Averages from Squat Lift
20
Integrated EMG (iEMG)

• important for quantitative EMG relationships (EMG
  vs. force, EMG vs. work)
• best measure of the total muscular effort
• useful for quantifying activity for ergonomic
  research
• various methods
• mathematical integration (area under absolute
  values of EMG time series)
• root-mean-square (RMS) times duration is similar
  but does not require taking absolute values
• electronically (see next page)

21
Electronically Integrated EMG

• always requires full-wave rectification
• various methods
• simple time integration (eventually saturates
  amplifier)
• integration and reset after a fixed time interval
• integration and reset after a particular value is
  reached
• cannot recognize artifacts, noise become included
• especially important to first remove DC-offsets
• must compute amount of iEMG from amplitude or
  differences between 2 amplitudes

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Sample Integrated EMG

• raw (band-passed filtered) EMG
• integrated EMG (over contraction)

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Other iEMGs

• integrate after preset time (0.1 s)
• integrate after preset voltage (20 mV.s)

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Frequency Spectrum

• useful for determining onset of muscle fatigue
• mean or median frequency of spectrum in
  unfatigued muscle is usually between 5080 Hz
• as fatigue progresses fast-twitch fibres drop
  out, shifting frequency spectrum to left
  (lowering mean and median frequencies)
• mean frequency is less variable and therefore is
  better than median
• useful for detecting neural abnormalities

25
Sample Power Spectrum

• flexor digitorum longus (MVC)

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Fatigue Analysis

• essentially a series of frequency analyses
• select duration of window (1 to 5 s)
• can overlap intervals to increase resolution
• usually normalized to percentage of initial mean
  or median frequency
• mean frequencies are less variable than median
• need to decide a threshold for when fatigue
  occurs (i.e., fatigue has occurred when mean or
  median frequency is below a threshold)

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Sample Fatigue Analysis

• erector spinae over 60 seconds (50 overlap)

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Amplitude Probability Distribution Function (APDF
CAPDF)

• developed by Hagberg Jonsson for ergonomics
  research (Ergonomics, 18311-319)
• EMG is amplitude normalized to MVC then sampled
  to compute frequencies of various amplitudes,
  usually for long durations (hours)
• Cumulative APDF is calculated to compute three
  thresholds
• 10tile lt 25 MVC for level of rest
• 50tile lt 1014 MVC for work load
• 90tile lt 5070 MVC for heavy contractions

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Sample APDF CAPDF

• neck flexor (only 5 minutes)

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Muscle Fibre Conduction Velocity

• requires two amplifiers and three electrodes
• electrodes are arranged in a line over a known
  distance (15 mm)
• middle electrode connected as ground to both
  amplifiers
• divide distance between electrodes by time
  difference between similar peaks (Andreassen
  Arendt-Neilsen, J Physiology, 319561-71, 1987)

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Wavelet Analysis

• decomposition of EMG time series into a
  time-frequency space, to determine the dominant
  modes of variability and their temporal changes
• figure shows EMG signal and
• its wavelet transform (SIMI)
• used to de-noise EMG signals,
• to detect fatigue and for feature
• extraction

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Other Techniques

• auto-correlation (correlate signal with itself
  shifted in time, gives signal characteristics)
• cross-correlation (correlate signal with another
  EMG signal, tests for crosstalk)
• zero-crossings (the more crossings the greater
  the level of recruitment)
• spike (peak) counting (number of spikes above a
  threshold)
• single motor unit detection
• double differential amplifier (velocity of
  propagation)