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to develop epilepsy for example the Unverricht-Lundborg disease. The majority
of patients who have seizures are first treated with anti-epileptic drugs (AEDs).
About 70% to 80% of these patients will become seizure free after the AED treat-
ment. The choice of the AEDs depends on several factors, including the type of
seizures, the age of the subject, and the potential side effects of the medicine.
Some patients, however, do not respond to the usual pharmacological treatments
and will then consider undergoing the epilepsy surgery.
14.3. Data Information
In the study, the EEG recordings (Table 14.1) were obtained using bilaterally,
surgically implanted electrodes in the hippocampus, temporal and frontal lobe
cortexes of the brain. The subject in this study was determined to have both left
and right hippocampal regions (bi-lateral) as epileptic seizure onset zones by neu-
rologists. The EEG recordings were meant for pre-surgical clinical evaluation
for possible surgical treatment of intractable temporal lobe epilepsy (TLE). The
recordings were obtained using the Nicolet BMSI 4000 with amplifiers of an input
range of 0.6 mV, sampling rate of 200 Hz and filters with a frequency range of a
0.5-70 Hz. The recording included a total number of 30 intracranial electrodes (8
subdural and 6 hippocampal depth electrodes for each cerebral hemisphere, and
a strip of 2 additional electrodes, see Fig. 14.1). The recorded EEG signals were
digitized and stored on magnetic media for subsequent off-line analysis.
Table 14.1.
EEG Data information
Patient No.
Gender
Age
Number of
Seizure
Duration of EEG
Electrodes
Onset Zone
Recordings (hours)
1
Male
37
30
L./R. Hippocampus
22
14.4. Graph-Theoretic Modeling for Brain Connectivity
Although the underlying mechanism of the transition from normal to seizure onset
for human brains is still largely unknown, the studies have reported that certain
type of temporal lobe seizures (TLE) is initiated by specific brain connectivity
patterns [25, 27]. The brain connectivity can be studied using a broad range of
network approaches. Several studies have attempted to use graph-theoretic meth-
ods to model the brain connectivity [2, 15, 18], especially the theory of directed
graphs is of special interest as it applies to structural and functional brain con-
nectivity at all levels. Applying the graph-theoretic models for the EEG signals,
the brain connectivity is presented using a complete graph
G
(
V,E
),where
V
is
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