Application to biomedical signals - Practical Biomedical Signal Analysis Using MATLAB - page 203

secutive MUAPs are not identical due to the variability of these arrival times caused
by the small fluctuations in the synaptic transmission called jitter. In the healthy
muscle, jitter is small (for biceps brachii around 20 μs ) and for small force level the
MUAPs generated by the given MU are easily identifiable by visual inspection.
FIGURE 4.49: a) Motor unit, b) cross-section of a muscle and a needle electrode.
Fibers belonging to the same MU are marked by the same shade.
A needle electrode typically detects the activity of several muscle fibers within
its pick-up area, which belong to a few different MUs (Figure 4.49 b). Usually the
shapes of MUAPs are different, since they depend on the geometrical arrangement
of the fibers of given MU with respect to the electrode, thus at low force levels
single MUAPs can be easily distinguished. During the steady muscle contraction
MUs fire repetitively, generating MUAP trains with essentially constant mean firing
rate. With the increasing force of muscle contraction, MU firing rate increases and
additional MUs are recruited, so the probability of superposition of single MUAPs
increases and the EMG shows a rise of amplitude and density (zero crossings of
voltage). At strong muscle contractions a so-called interference pattern develops and
EMG resembles stochastic signal. More detailed analysis of the relationship between
EMG and muscle force can be found in [Piotrkiewicz, 1999].
In neuromuscular diseases, the shapes of MUAPs change ( Figure 4.50) , which
makes their study useful for clinical diagnosis. Two main types of neuromuscular dis-
eases are distinguished: neurogenic and myogenic. In neurogenic diseases the patho-