Abstract
Patient-specific epilepsy seizure detectors were
designed based on the genetic programming artificial
features algorithm, a general-purpose, methodic
algorithm comprised by a genetic programming module and
a k-nearest neighbour classifier to create synthetic
features. Artificial features are an extension to
conventional features, characterised by being
computer-coded and may not have a known physical
meaning. In this paper, artificial features are
constructed from the reconstructed state-space
trajectories of the intracranial EEG signals intended
to reveal patterns indicative of epileptic seizure
onset. The algorithm was evaluated in seven patients
and validation experiments were carried out using 730.6
hr of EEG recordings. The results with the artificial
features compare favourably with previous benchmark
work that used a handcrafted feature. Among other
results, 88 out of 92 seizures were detected yielding a
low false negative rate of 4.35percent
- algorithms,
- artificial
- classification,
- classifier,
- detection,
- detectors,
- diseases,
- electroencephalography,
- epilepsy
- epileptic
- features,
- genetic
- genetically
- hr,
- k-nearest
- medical
- neighbour
- patient-specific
- processing,
- programmed
- programming,
- reconstructed
- reconstruction730.6
- seizure
- signal
- state-space
- trajectories
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