Biomedical Engineering Reference
In-Depth Information
FIGURE 4.16: A comparison of the evolution of a seizure analyzed by means of
correlation integral and matching pursuit. Top panel: the time sequence of plots of
the correlation integral log C
(Sect. 2.5.2) obtained from 10-s epochs
of the signal from rat hippocampus in the peri-ictal time. Adapted from [Pijn et al.,
1997]. Bottom panel: Time frequency representation obtained by means of MP from
the HCR signal shown in top panel and repeated at the bottom of the figure. From
[Blinowska, 2002].
(
ε
,
m
)
vs log
(
ε
)
and arbitrary time lag, average phase amplitude (calculated by Hilbert transform),
phase angle, and amplitude dispersion. The authors distinguished rhythmic partial
onset, tonic middle and clonic terminal activity. In respect to the postulated hyper-
synchronous character of seizures they reported that synchronization was a promi-
nent feature only once the seizure had passed through its initiation phase and was a
variable feature of seizure termination depending on the subject. We have to distin-
guish here local synchronous activity of neurons which leads to the high amplitude
of epileptic activity and hyper-synchronization in the larger spatial scale.
An interesting methodological approach to the assessment of synchronization of
interictal and ictal EEG signals was presented by [Franaszczuk and Bergey, 1999].
They have used the multichannel autoregressive method of analysis that can be inter-
preted in stochastic and deterministic framework. As a measure of synchronization
they have used a value connected with the goodness of fit of MVAR model:
log det
)
V
SY
=
(
(4.3)
is a determinant of the residual matrix V of MVAR (Sect. 3.2). For a
purely uncorrelated Gaussian normalized white noise, V is a diagonal identity ma-
V
where det
(
))
 
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