Epileptic Seizure Detection Using Genetically Programmed Artificial Features

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@Article{Firpi:2007:BE,

• title = "Epileptic Seizure Detection Using Genetically Programmed Artificial Features",
• author = "Hiram Firpi and Erik D. Goodman and Javier Echauz",
• journal = "IEEE Transactions on Biomedical Engineering",
• year = "2007",
• volume = "54",
• number = "2",
• pages = "212--224",
• DOI = "doi:10.1109/TBME.2006.886936",
• ISSN = "0018-9294",
• month = feb,
• keywords = "genetic algorithms, genetic programming, diseases, electroencephalography, medical signal detection, medical signal processing, signal classification, signal reconstruction730.6 hr, epileptic seizure detection, genetic programming, genetically programmed artificial features, k-nearest neighbour classifier, patient-specific epilepsy seizure detectors, reconstructed state-space trajectories",
• 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",

}

Genetic Programming entries for Hiram A Firpi Erik Goodman Javier Echauz

Citations