18-12-2012, 03:03 PM
An Epileptic Seizures Detection Algorithm based on the Empirical Mode Decomposition of EEG
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INTRODUCTION
PILEPSY is a chronic neurological disorder that affects
around 50 million people worldwide of all ages [1]. This
brain disorder is characterized by recurrent seizures which
are the clinical manifestations of sudden, usually brief,
excessive electrical discharges in a group of brain cells.
Different parts of the brain can be the source of such
discharges. Epilepsy responds to antiepileptic drugs about
70% of the cases and the remaining affected individuals
could benefit from surgical therapy [1].
The seizure detection is an important component in the
diagnosis of epilepsy. This includes visual scanning of
Electroencephalogram (EEG) long recordings which is very
time consuming and the conclusions are very subjective so
disagreement between physicians are not rare. For this
reason, the computerized analysis of EEG signals using
automatic algorithms is highly useful for the diagnosis of
this disease.
MATERIALS
The EEG database contains invasive EEG recordings of
21 patients suffering from medically intractable focal
epilepsy. The data were recorded during invasive presurgical
epilepsy monitoring at the Epilepsy Center of the
University Hospital of Freiburg, Germany [13]. In order to
obtain a high signal-to-noise ratio, fewer artifacts, and to
record directly from focal areas, intracranial grid-, strip-,
and depth-electrodes were used. The EEG data were
acquired using a Neurofile NT digital video EEG system
with 128 channels, 256 Hz sampling rate, and a 16 bits A/D
converter. Notch or band pass filters have not been applied
in the acquisition stage.
The available EEG records include only 6 channels (3
focal electrodes and 3 extrafocal electrodes). The records are
divided into segments of 1hour long. In this study, the 3
intra source records of 9 patients with focal epilepsy
originated in the temporal lobe region were selected. This
computes a total of 90 segments per each channel, 51 of
them without epileptic seizures and 39 segments denoted as
having only one epileptic seizure each.
RESULTS
The seizure detection algorithm was applied to a total of
90 EEG segments with 39 epileptic seizures, corresponding
to 9 patients of Freiburg’s data base with focal epilepsy
originated in the temporal lobe region.
In order to evaluate the performance of the method the
following diagnostic categories were considered on the
detection stage: true negative (TN), false positive (FP), true
positive (TP), false negative (FN). These indexes are shown
quantitatively for each patient in Table I.