This is an excerpt from Research Methods in Biomechanics-2nd Edition by Gordon E. Robertson,Graham E. Caldwell,Joseph Hamill,Gary Kamen & Saunders Whittlesey.
EMG and Low Back Pain
The diagnosis and treatment of low back pain are important issues. In the United States, for example, back pain accounts for two-thirds of all workers compensation costs. Peripheral muscle problems may be one issue; atrophy of the multifidus is frequently observed in patients with low back pain (Hides et al. 1994). EMG is becoming an increasingly useful tool for the diagnosis and treatment of low back pain.
The trunk musculature plays an important role in tasks such as lifting and throwing. During these kinds of tasks as well as in other activities that might threaten the postural control of the individual and perturb balance, the nervous system implements strategies to activate trunk muscles while performing voluntary movements involving remote muscle groups. However, the prevalence of back pain in the general population, and the need to identify ergonomically efficient and safe ways to use back muscles, require that we understand the nature of activation in muscles controlling the trunk. Here, too, EMG is an important tool.
A number of issues render EMG recordings from the trunk musculature difficult to obtain and interpret. From an anatomical viewpoint, the architecture of the back muscles is complex. For example, it is generally accepted that injury or disorder of the multifidus frequently results in back pain. Both multifidus and erector spinae have distinct superficial and deep portions (Bustami 1986; Macintosh et al. 1986) with different histochemical and biomechanical characteristics (Bogduk et al. 1992; Dickx et al. 2010). In some respects, it is fortunate that most of the muscle mass of the multifidus lies in the more superficial portion, so that surface EMG has a greater possibility of characterizing the fibers that produce large amounts of force. However, the multifidus superficial portion has a different role than the deeper fibers. Fibers in the superficial portion cross numerous spinal segments and are thus in a better position to produce back extension. However, the deeper fibers are relatively short, crossing perhaps one or two segments, and thus protect segments of the lumbar spine from inappropriate shear or torsion torques (Macintosh and Valencia 1986).
In addition to issues concerning the activation of the back extensor muscles, a number of important questions require resolution by EMG analysis in which considerable electrocardiographic (ECG) artifact may be present. ECG artifact is particularly problematic during relatively low force contractions, exaggerating the ratio between the signal of interest and the interfering ECG signal. Recording EMG signals from abdominal, knee extensor, and other trunk muscles during normal human movements, for example, can result in the placement of electrodes well within recording range of the electrocardiogram. The relatively large ECG signal can exaggerate the amplitude of EMG activity, and the low-frequency characteristics of the ECG wave can also alter the frequency characteristics of the recorded EMG activity.
For example, the absence of sufficient trunk activity can produce instability resulting in low back pain (van Dieën et al. 2003). However, the amplitude of EMG activity required to ensure stability is small and thus can be affected by the ECG signal (Cholewicki et al. 1997). The best solution is to place the electrodes in a location from which no or minimal ECG artifact can be recorded. However, this is frequently difficult if not impossible. Consequently, numerous algorithms have been suggested to remove the ECG artifact.
One frequently implemented technique to remove this artifact is to compute a template of the ECG signal and subtract it from the electromyogram. This has been proved to be a reasonably successful procedure and has been implemented in studies recording from the diaphragm (Bartolo et al. 1996) as well as rectus abdominis (Hof 2009). Hof (2009) described a technique in which the ECG signal is recorded simultaneously with the EMG signal of interest, and template subtraction is then implemented (figure 8.15).
Digital filtering is another useful alternative for ECG artifact removal. Drake and Callaghan (2006) used a FIR (finite impulse response) filter with a hamming window, although they concluded that the most efficient filtering result could be obtained using a somewhat simpler fourth-order, 30 Hz high-pass cutoff Butterworth filter. Template subtraction improved extraction of the ECG signal but required a large amount of time.
Alternative ECG removal techniques include the use of adaptive filters (Lu et al. 2009; Marque et al. 2005), wavelet-independent component analysis (Taelman et al. 2007), and wavelet-based adaptive filters (Zhan et al. 2010). Irrespective of the technique used for ECG signal artifact removal, it is apparent that the resultant improvement in signal interpretation can be important. Hu and colleagues (2009), for example, found that the use of independent component analysis to remove artifact resulted in an improved ability to discriminate between patients with low back pain and normal subjects during both sitting and standing tasks.
Analysis of the EMG activity in back muscles has produced some interesting results, in part reflecting the unique anatomical issues discussed here. As long ago as 1962, Morris and colleagues noted that “the three muscles of the erector spinae group considered here . . .
do not always show parallel activity, and one may be active while the other two are inactive” (p. 519). This may suggest that the mere demonstration of greater or lesser EMG amplitude may not be indicative of abnormality in a particular muscle. Potentially more important than EMG amplitude is the timing of EMG activity. Deep and superficial portions of the multifidus are differentially active during arm movements (Moseley et al. 2002). One suggestion is that the manner in which the multifidus is differentially controlled in people with back pain compared with those without may be the source of chronic back pain (MacDonald et al. 2009).
Similarly, the analysis of EMG frequency characteristics in patients with back pain compared with control subjects has suggested that there may be differences between muscles and even differences within muscles. Patients with low back pain frequently exhibit alterations in the electromyographic response to fatigue in the back extensor muscles (Biedermann et al. 1991). This is one area in which spectral analysis of the EMG signals has been helpful. As in other muscles, median EMG frequency decreases in the trunk extensors with fatigue (Demoulin et al. 2007). It is interesting to note that Kramer and colleagues (2005) found that the magnitude of median frequency decrease was greater in healthy subjects than in individuals with back pain. Support for importance of electrode placement is obtained from the observations of Sung and colleagues (2009). Normal subjects and patients with back pain underwent an isometric contraction protocol that induced fatigue of the back extensors. EMG frequency measurements made in both the thoracic and the lumbar portions of the erector spinae indicated that the thoracic portion had a significantly lower median frequency than the lumbar portion in patients with low back pain. However, median frequency was lower in the lumbar portion than in the thoracic portion in control subjects. Thus, in the analysis of trunk musculature that might be involved in the development of back pain, EMG studies using wire electrodes frequently may be required to record from different portions of the muscle.
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