Application to biomedical signals - Practical Biomedical Signal Analysis Using MATLAB - page 130

ods such as single photon emission computer tomography (SPECT), and high res-
olution MRI are useful. However, there are epileptic foci elusive to these imaging
methods. In such cases EEG or MEG are indispensable.
The characteristics of epileptic seizure are determined based on the region of brain
involved and the underlying epileptic condition. Two main classes of seizures are
distinguished:

1. Partial (focal) seizures. They arise from an electric discharge of one or more
localized areas of brain and then possibly they are secondarily generalized.
They may or may not impair consciousness.

2. Generalized seizures. In this case electrical discharge involves the whole brain.
The symptoms could be absence of consciousness (petit mal) or loss of con-
sciousness connected with muscle contraction and stiffness (grand mal).

Digital analysis of epileptic activity serves different purposes:

• Quantification of the seizure

• Seizure detection and prediction

• Localization of an epileptic focus

The above problems will be addressed in the following paragraphs.

4.1.6.4.1 Quantification of seizures Investigations of seizure dynamics are con-
ducted with the aim to answer some of the most fundamental and yet debatable ques-
tions in epileptology like how the propagating ictal discharges affect the ongoing
electrical activity of the brain or why seizures terminate. Seizure dynamics have been
investigated using many different mathematical methods, both linear and non-linear.
Time course of seizure is a non-stationary process typically evolving from higher to
lower frequencies; therefore the methods of time-frequency analysis are appropriate
to estimate the seizure dynamics.
The most advanced method of time-frequency analysis—matching pursuit (Sect.
2.4.2.2.7) was used by [Franaszczuk and Bergey, 1999] to evaluate seizures originat-
ing from the mesial temporal lobe. Periods of seizure initiation, transitional rhyth-
mic bursting activity, organized bursting activity, and intermittent bursting activity
were identified ( Figure 4.15) . The authors showed in a subsequent study [Bergey
and Franaszczuk, 2001] that the complexity of the EEG signal registered from the
location closest to the epileptic focus increases during a seizure.
The non-linear method based on the computation of the correlation dimension D 2
was applied by [Pijn et al., 1991] for analysis of the time course of EEG recorded
from different sites of the limbic cortex of rats. A comparison of the evolution of a
seizure analyzed by means of correlation integral [Pijn et al., 1997] and matching
pursuit 1 is shown in Figure 4.16 [Blinowska, 2002].

1 Matching pursuit is a method in which no assumption about linearity or non-linearity of the signals is
required.