Evolutionary Algorithms and Frequent Itemset Mining for Analyzing Epileptic Oscillations

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Smart_Otis_L_200705_phd,
  author =       "Otis Lkuwamy Smart",
  title =        "Evolutionary Algorithms and Frequent Itemset Mining
                 for Analyzing Epileptic Oscillations",
  school =       "Electrical Engineering, Georgia Institute of
                 Technology",
  year =         "2007",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, PSO",
  size =         "approx 202 pages",
  URL =          "http://smartech.gatech.edu/bitstream/1853/22709/1/Smart_Otis_L_200705_phd.pdf",
  abstract =     "The objective of this research is to develop an
                 automated methodology that maps epileptic networks in
                 patients with epileptic seizures. Epilepsy afflicts
                 over 60 million people worldwide with limited options
                 for treatment, one-third of who have seizures that
                 cannot be controlled by antiepileptic drugs (AED's).
                 For 7percent of patients with epilepsy, surgery is an
                 option because their seizures reliably begin in one
                 discrete region. However, effective surgical procedures
                 typically require removing a significant volume of
                 brain tissue due to the lack of a reliable method for
                 pinpointing the exact location of seizure onset and the
                 pathways through which seizures are generated and
                 spread. Consequently, medical or surgical treatment
                 cannot control or remedy epilepsy in over 15 million
                 individuals with the disorder, partly due to poorly
                 localised or multi-focal seizure onsets. Although new
                 technologies based upon preemptive electrical
                 stimulation, the infusion of drugs, or focal cooling
                 may be viable alternatives to surgical resection for
                 patients with intractable seizures, these approaches
                 still require a reliable means to map epileptic
                 networks and determine the best sites for therapeutic
                 intervention.",
  abstract =     "It has been suggested that certain epileptic high
                 frequency oscillations within the intracranial
                 electroencephalogram (IEEG) both between seizures
                 (interictal) and preceding a seizure (pre-ictal) may
                 localise regions important to seizure generation on the
                 IEEG. The work below aims to map epileptogenic tissue
                 by detecting epileptic oscillations in the IEEG and
                 statistically identifying regions of brain in which the
                 oscillations are concentrated. The methodology to
                 accomplish this aim incorporates two primary
                 components: 1) a module that detects the oscillations
                 using an feature via evolutionary algorithms; and 2) a
                 module that statistically filters the output of the
                 first module using frequent itemset mining (FIM), which
                 discovers chronic patterns across electrodes. Together,
                 these components provide decision-support for an expert
                 to map networks in the brain that generate seizures and
                 guide a suitable therapy for patients.

                 Prior attempts to accurately detect similar
                 electrophysiological events in the IEEG have exhibited
                 marginal performance. This may be because the methods
                 lack robust choices for the selection, extraction, and
                 combination of features that effectively distinguish
                 epileptic events from non-epileptic events. This
                 research successfully implements evolutionary
                 algorithms (i.e., genetic programming, particle swarm
                 optimisation) to detect epileptic oscillations in data
                 with very poor signal-to-noise ratio (SNR) even after
                 filtering and a low bandwidth up to 100 Hz.",
  abstract =     "The presented detector achieves median values near and
                 above 80percent sensitivity and 85percent specificity
                 over a sample of testing records consisting of 10-16
                 records with at least 30 epileptic oscillations per
                 record across six subjects. An analysis of variance
                 (ANOVA) reveals that the presented method outperforms
                 (in terms of sensitivity and specificity) a naive
                 method, which represents a standard to detect epileptic
                 oscillations using an energy feature and a threshold.
                 Permutation testing demonstrates that the method
                 incorporating features returned by an evolutionary
                 algorithm (EA) does not perform equal to random chance
                 at P-values near zero. The FIM clusters electrodes with
                 high concentrations of epileptic oscillations, where
                 most couplings overlap with areas of ictal (seizure)
                 onset and epileptogenic zones defined by clinically by
                 expert physicians.

                 These findings and results are significant, considering
                 that this particular field of study in epilepsy is not
                 established, meaning that this research represents some
                 of the first efforts to investigate that epileptic high
                 frequency oscillations and that no definitive
                 benchmarks have been established. Optimistically, this
                 work provides a benchmark for detecting and analysing
                 epileptic oscillations. This research contributes an
                 efficient means to create and fuse quality features,
                 techniques to evaluate the quality of a feature, and an
                 improved method to detect abnormal physiological events
                 key to reliably mapping regions of dysfunctional brain
                 that constitute epileptic networks. The ultimate goal
                 is to use this new knowledge to understand the
                 generation of seizures in these individuals and disrupt
                 the mechanism. Hopefully, this research will lead to a
                 better control of seizures and an improved quality of
                 life for the millions of persons affected by
                 epilepsy.",
  notes =        "Copyright Otis L. Smart 2007",
}

Genetic Programming entries for Otis L Smart

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