Electromyogram
233
is mostly in the higher frequencies, while, after fatigue, the power spectrum shifts
toward lower frequencies. The shift in the dominant frequencies in each of the states,
rest and contraction, is an indication of the muscle status.
The use of wavelet analysis of EMG signals shows advantages in the detection of
changes in the wave patterns during stimulated recordings. For instance, isometric
contraction is controlled by the patient and can be performed in various modes.
Rapid contraction will have a different EMG pattern than slow initiation of the
contraction.
Wavelet analysis is also applied to detect the presence or absence of some expected
patterns in healthy and abnormal cases. Wavelet analysis is also used to decompose
the signal and detect the delays in response to the stimulations. Specifically, detection of the delays in the response times of motor units using wavelet and STFT can
help identify the state, size, and of the density of the motor units involved in the neuromuscular task. Large motor units are generally faster in response compared to the
smaller motor units. Daubechies wavelets have been shown useful; however, other
wavelets can reveal details in different aspects of the signal structure and elaborate
on the muscle recruitment process.
11.7 SUMMARY
In this chapter, we first described the origin of electromyogram (EMG) and the way
this signal is formed and measured. Then we briefly reviewed the applications of
EMG in diagnostics of several neuromuscular diseases. Finally, we reviewed the
main time-, frequency-, and wavelet-domain methods for filtering, feature extraction,
and analysis of EMG.
ACKNOWLEDGMENT
The source of all EMG signals used in this chapter is Motion Lab Systems, Inc.,
Baton Rouge, LA; Courtesy: Edmund Cramp.
PROBLEMS*
11.1 Import the data in the file “p_11_1.txt” in MATLAB ® and plot the signal.
In order to do so, use File/Import Data … on the main MATLAB menu and
follow the steps in loading and naming of the data. The file contains one
single muscle signal from a 40 month old patient; the first column is the time,
and the second column is the data.
a. Determine the frequency spectrum or power spectrum.
b. Using “wavemenu” and Daubechies 1 mother wavelet, denoise the signal
and locate significant features of the EMG signal.
c. Calculate the AVR value of the EMG signal.
* Problems 11.1 through 11.5 use data obtained from http://www.physionet.org/physiobank/database/
gait-maturation-db/
233
is mostly in the higher frequencies, while, after fatigue, the power spectrum shifts
toward lower frequencies. The shift in the dominant frequencies in each of the states,
rest and contraction, is an indication of the muscle status.
The use of wavelet analysis of EMG signals shows advantages in the detection of
changes in the wave patterns during stimulated recordings. For instance, isometric
contraction is controlled by the patient and can be performed in various modes.
Rapid contraction will have a different EMG pattern than slow initiation of the
contraction.
Wavelet analysis is also applied to detect the presence or absence of some expected
patterns in healthy and abnormal cases. Wavelet analysis is also used to decompose
the signal and detect the delays in response to the stimulations. Specifically, detection of the delays in the response times of motor units using wavelet and STFT can
help identify the state, size, and of the density of the motor units involved in the neuromuscular task. Large motor units are generally faster in response compared to the
smaller motor units. Daubechies wavelets have been shown useful; however, other
wavelets can reveal details in different aspects of the signal structure and elaborate
on the muscle recruitment process.
11.7 SUMMARY
In this chapter, we first described the origin of electromyogram (EMG) and the way
this signal is formed and measured. Then we briefly reviewed the applications of
EMG in diagnostics of several neuromuscular diseases. Finally, we reviewed the
main time-, frequency-, and wavelet-domain methods for filtering, feature extraction,
and analysis of EMG.
ACKNOWLEDGMENT
The source of all EMG signals used in this chapter is Motion Lab Systems, Inc.,
Baton Rouge, LA; Courtesy: Edmund Cramp.
PROBLEMS*
11.1 Import the data in the file “p_11_1.txt” in MATLAB ® and plot the signal.
In order to do so, use File/Import Data … on the main MATLAB menu and
follow the steps in loading and naming of the data. The file contains one
single muscle signal from a 40 month old patient; the first column is the time,
and the second column is the data.
a. Determine the frequency spectrum or power spectrum.
b. Using “wavemenu” and Daubechies 1 mother wavelet, denoise the signal
and locate significant features of the EMG signal.
c. Calculate the AVR value of the EMG signal.
* Problems 11.1 through 11.5 use data obtained from http://www.physionet.org/physiobank/database/
gait-maturation-db/