Biomedical Engineering Reference
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FIGURE 4.15: (SEE COLOR INSERT)
From the bottom: signal, 2D time-
frequency energy distribution obtained by means of MP, the same distribution in
3D. Courtesy of P. Durka, from [Durka, 2003].
At the beginning of the seizure (20-40 s) the occurrence of epileptic spikes re-
sulted in a low value of
D
2
; in time-frequency energy distribution it was reflected
by very broad-band structures of short duration. During the period of chaotic behav-
ior (60-80 s) characterized by flatness of the plot we can see random distribution
of time-frequency structures. Interesting is the period (150-170s) when the system
tends toward limit cycle, which is accompanied by a low value of
D
2
. We can con-
clude that time-frequency distribution obtained by MP algorithm reveals the dynam-
ics of the signal and explains the behavior of
D
2
preventing its misinterpretation.
The problem of spatial synchronization in the pre-ictal and ictal periods has at-
tracted a lot of attention. It is usually approached by assessing the correlation struc-
ture of multichannel EEG. Schindler [Schindler et al., 2007] found that the zero-lag
correlations of multichannel EEG either remain approximately unchanged or, espe-
cially in the case of secondary generalization, decrease during the firsthalfofthe
seizure, then gradually increase before seizure termination. However, zero-lag cor-
relation doesn't give the full information on synchronization phenomena since there
are phase differences between channels connected with the propagation of epileptic
activity.
In [Schiff et al., 2005] canonical multivariate discrimination analysis based on sin-
gular value decomposition was applied to search for dynamically distinct stages of
epileptic seizures. The input values were: total power, total correlation at both zero
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