Application to biomedical signals - Practical Biomedical Signal Analysis Using MATLAB - page 217

11 decomposed MUAPs of the signal from one sensor were locked in with firings of
11 corresponding MUAPs decomposed from the other sensors' signals.
FIGURE 4.57: Illustration of “reconstruct-and-test” procedure for assessing the
accuracy of the decomposition algorithm. An actual sEMG signal s(n) is decomposed
to identify its MUAPTs (MUAP templates). Signal y(n) is synthesized by summing
together the decomposed MUAPTs of s(n) and white Gaussian noise whose variance
is set equal to that of the residual signal from the decomposition. The reconstituted
y(n) signal is then decomposed and compared to the decomposed MUAPTs of s(n).
The ellipses indicate discrepancies between the MUAPTs of y(n) and s(n). These are
designated as errors. From [Nawab et al., 2010].
The method seems to be promising in respect to potential clinical applications,
since the authors claimed that the system is able to detect morphology of the shapes
of up to 40 concurrently active motor units without relocating the sensor and that the
polyphasic MUAPs may be detected with the proposed technology.
The view on applicability of sEMG in clinical practice has changed over the years.
In a technology assessment study issued by the American Association of Electrodi-
agnostic Medicine [Haig et al., 1996] it was concluded that there is no evidence to
support the use of sEMG in the clinical diagnosis and management of nerve or mus-
cle disease. However, the recent study based on the literature covering the period
1994-2006 [Meekins et al., 2008] stated that sEMG may be useful to detect the pres-
ence of some neuromuscular diseases and a study of fatigue, but there are insufficient
data to support its utility for distinguishing between neuropathic and myopathic con-
ditions. This statement creates a challenge for the scientist working in the field of
sEMG to develop better techniques and signal processing methods.