Tuesday, July 26, 2011

Systems of analyzing and diagnosing patients' physical abilities after implantation

This measurement from a physiological point of view enables the patient a full range of
motion. The orthopaedic surgeons claim that implanted people cooperation between the
ball, which replaces the spherical head of the femur and the cup, which replaces the worn
out hip socket is significant in case of long duration of the setting of prosthesis as well as
their later mobility. In case of people after implantation.
Nowadays, the average age of people having a hip joint implantation is still dropping and so
far it seems to be important to deal with this topic. As I mentioned earlier, so far there is not
ideal and cheap and fulfilling all functions of diagnostic and analyzing instrument mentioned
above. Most of the equipment / devices available on the Polish market is even if extremely
expensive do not fulfill the requirements.In most cases surgeons, orthopedics, or physical
therapists facilitate their work simplifying diagnostics and movement analysis in order to use
the methods described above selectively.In most cases it is not connected with any health
hazard a professional incompetence. However, nobody knows if for instance a broad scope


of joint movement will not lead to contractures of particular groups of muscles.The analysis
of these methods and tests /researches will help to state / determine basic requirements
needed to judge implanted people .
In this report, statistical time series analysis of nonstationary EEG/MEG data is proposed.
The signal is investigated as a stochastic process, and approximated by a set of
deterministic components contaminated by the noise which is modelled as a parametric
autoregressive process. Separation of the deterministic part of time series from stochastic
noise is obtained by an application of matching pursuit algorithm combined with testing for
the residuum's weak stationarity (in mean and in variance) after each iteration. The method
is illustrated by an application to simulated nonstationary data.
In brain evoked activity measured by means of EEG/MEG, one can observe time-dependent
changes of its various characteristics like amplitude and frequency, as well as the
contaminating noise. For this reason, it is necessary to use the analysis methods designed
for nonstationary signals, since the standard EEG/MEG methodology based on signal
averaging and simple spectral analysis is insufficient. Time-frequency estimation
methods such as short-time Fourier transform, Wigner distribution, or discrete and
continuous wavelet transform are very useful, yet, statistically inefficient. A time series (TS)
model for the observed data {z(t)} is a specification of the joint distributions (or possibly of
only the means and covariances) of a sequence of random variables {Z , with a realization
denoted by {z(t)} [1]. In a short form, an additive TS model can expressed by the sum of
deterministic d(t) and stochastic l(t) components. 
N sine waves or other non-commensurable periodic functions (or commensurable but with a
period much longer than the periods of its particular components) s(t) and a stationary
noise e(t).  evoked-response generative process given by these methods is incomplete.
In this research, EEG/MEG signal is investigated as a stochastic process which can be
decomposed to a set of deterministic functions representing its nonstationarity and
stationary residua. For modelling the stochastic.

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