Application to biomedical signals - Practical Biomedical Signal Analysis Using MATLAB - page 133

trix 2 ,and SY

1 setting the upper bound value for SY . For a purely deterministic
linear system or a dynamical system of a periodic or quasi-periodic trajectory the ma-
trix V represents a covariance matrix of measurement errors, setting the lower bound
value of SY . For chaotic or stochastic colored-noise systems the value of SY will
be between these bounds. The quantity SY can be interpreted as a measure of order
in the system. There is a close relationship between Shannon entropy and residual
variance of an autoregressive process [Serio, 1992]. In case of multichannel process,
high correlation between channels increases predictability. If channels are highly
correlated, one channel can be predicted using other channels, the number of vari-
ables necessary to describe dynamics of the system is lower, and the MVAR is better
fitted, resulting in smaller values of SY . The changes to lower values of SY reflect
higher spatiotemporal synchronization. The method was tested on a limited number
of patients, however the results were very coherent. The relatively high and station-
ary level of SY in the interictal EEG remote from seizure onset reflected much less
synchronization. The minimum of SY always occurred shortly after the onset of a
seizure, reflecting high regional synchrony. The level of synchronization remained
high after a seizure for prolonged periods which may explain in part the phenomena
of seizure temporal clustering often observed in patients. It seems that the method
has a high potential for explaining the evolution of seizures and can be helpful in
seizure detection [Jouny et al., 2005].

=

4.1.6.4.2 Seizure detection and prediction The study of epileptic EEG dynam-
ics can potentially provide insights into seizure onset and help in construction of
systems for seizure detection and prediction. A method capable of predicting the
occurrence of seizures from EEG would open new therapeutic possibilities. Namely
long-term medication with anti-epileptic drugs could move toward EEG-triggered on
demand therapy (e.g., excretion of fast-acting anticonvulsant substances or electrical
stimulation preventing or stopping the seizure). The problem of forecasting seizures
is not yet satisfactorily resolved which raises the question of whether the prediction
is feasible at all.
There are two different scenarios of seizure generation [Lopes da Silva et al.,
2003b]. One implies sudden and abrupt transition, in which case a seizure would
not be preceded by detectable changes in EEG. The model describing this scenario
assumes existence of a bi- or multistable system where the jump between coexisting
attractors takes place, caused by stochastic fluctuations [Suffczynski et al., 2004].
This model is believed to be responsible for primary generalized epilepsy. Alterna-
tively the transition to epileptic state may be a gradual process by which an increasing
number of critical interactions between neurons in a focal region unfolds over time.
This scenario, likely responsible for focal epilepsy, was modeled by [Wendling et al.,
2002].
Since the 1970s many methods have been devised with the purpose of forecast-
ing an epileptic seizure. They are critically reviewed in [Mormann et al., 2007]. The

2 Signal must be normalized by subtracting the mean value and devision by standard deviation.