Transcript Electromyography: Processing

Electromyography:
Processing
D. Gordon E. Robertson, PhD
Biomechanics Laboratory,
School of Human Kinetics,
University of Ottawa, Ottawa, CANADA
Types of Signal Processing
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Raw (with or without band-pass filtering)
Full-wave rectified (absolute value)
Averaged or root-mean-square (RMS)
Linear envelope
Ensemble-averaged
Integrated
Frequency or power spectrum (Fourier)
Fatigue analysis (sequential Fourier)
Amplitude probability distribution function
(APDF) and CAPDF
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Raw EMG
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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 -1
• “clean” signal
• with ECG crosstalk
heart rate
detected
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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
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Muscle Crosstalk
• one muscle’s EMG is picked up by another
muscle’s electrodes
• can be reduced by careful electrode
positioning
• can be determined by cross-correlation
• difficult to distinguish crosstalk from
synergistic contractions
• biarticular muscles have “extra” bursts of
activity compared to monoarticular muscles
(if so crosstalk is not a problem)
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Errors when Recording EMGs -2
• with line (AC) interference
60 Hz
noise
• with DC-offset or DC-bias
baseline
not at
zero volts
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Solutions
• To interference (of line and radio freq. 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 to remove in post-processing
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Errors when Recording EMGs -3
• with movement artifact
electrodes
were struck
• with amplifier saturation (+/–0.5 V)
clipped at
+/–0.5 V
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Solutions
• To movement artifacts
– Affix leads to subject (tape or wraps)
– 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” or “bottom” out
– Use larger range of A/D converter (+/–10 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 and 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)
can
have a
time lag
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Ensemble-Averaged EMG
• usually applied to cyclic activities and linear
envelope EMGs
• requires means for identifying start of cycle or
start and end of 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 cont’d
• amplitude normalization is often done
– to maximal voluntary contraction (MVC)
– to submaximal isometric contraction
– to EMG of a functional activity
• 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
mean +/–S.D.
abscissa must
be normalized
to % cycle
ordinate may also
be normalized
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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)
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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 will be included
• especially important to 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)
notice
units are
mV.s
read total
iEMG from
curve (i.e.,
320 mV.s)
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Other iEMGs
• integrate after preset time (0.1 s)
add each
peak to
get total
IEMG
notice
units are
mV.s
• integrate after preset voltage (20 mV.s)
multiply
number of
peaks by
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 50-80 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
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Sample Power Spectrum
• flexor digitorum longus (MVC)
gradual
increase to
>95% after
200 Hz
median
frequency
approx. 70 Hz
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Fatigue Analysis
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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)
gradual
decline of
mean and
median
frequencies
medians
are more
variable
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Amplitude Probability Distribution
Function (APDF & CAPDF)
• developed by Hagberg & Jonsson for
ergonomics research (Ergonomics, 18:311-319)
• 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:
– 10%tile < 2–5% MVC for level of rest
– 50%tile < 10–14% MVC for work load
– 90%tile < 50–70% MVC for heavy contractions
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Sample APDF & CAPDF
• neck flexor (only 5 minutes)
90%tile
=52%MVC
50%tile
=8%MVC
10%tile
=2%MVC
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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)
• peak counting (number of peaks above a
threshold)
• single motor unit detection
• double differential amplifier (velocity of
propagation)
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