Identification of epilepsy stages from ECoG using genetic programming classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Sotelo:2013:CBM,
  author =       "Arturo Sotelo and Enrique Guijarro and 
                 Leonardo Trujillo and Luis N. Coria and Yuliana Martinez",
  title =        "Identification of epilepsy stages from {ECoG} using
                 genetic programming classifiers",
  journal =      "Computers in Biology and Medicine",
  year =         "2013",
  volume =       "43",
  number =       "11",
  pages =        "1713--1723",
  month =        nov,
  email =        "leonardo.trujillo@tectijuana.edu.mx",
  keywords =     "genetic algorithms, genetic programming, Epilepsy
                 diagnosis, Classification",
  ISSN =         "0010-4825",
  URL =          "http://www.sciencedirect.com/science/article/pii/S001048251300231X",
  DOI =          "doi:10.1016/j.compbiomed.2013.08.016",
  size =         "11 pages",
  abstract =     "Objective: Epilepsy is a common neurological disorder,
                 for which a great deal of research has been devoted to
                 analyse and characterise brain activity during
                 seizures. While this can be done by a human expert,
                 automatic methods still lag behind. This paper analyses
                 neural activity captured with Electrocorticogram
                 (ECoG), recorded through intracranial implants from
                 Kindling model test subjects. The goal is to
                 automatically identify the main seizure stages:
                 Pre-Ictal, Ictal and Post-Ictal. While visually
                 differentiating each stage can be done by an expert if
                 the complete time-series is available, the goal here is
                 to automatically identify the corresponding stage of
                 short signal segments.

                 Methods and materials: The proposal is to pose the
                 above task as a supervised classification problem and
                 derive a mapping function that classifies each signal
                 segment. Given the complexity of the signal patterns,
                 it is difficult to a priori choose any particular
                 classifier. Therefore, Genetic Programming (GP), a
                 population based meta-heuristic for automatic program
                 induction, is used to automatically search for the
                 mapping functions. Two GP-based classifiers are used
                 and extensively evaluated. The signals from epileptic
                 seizures are obtained using the Kindling model of
                 elicited epilepsy in rodent test subjects, for which a
                 seizure was elicited and recorded on four separate
                 days. Results: Results show that signal segments from a
                 single seizure can be used to derive accurate
                 classifiers that generalise when tested on different
                 signals from the same subject; i.e., GP can
                 automatically produce accurate mapping functions for
                 intra-subject classification. A large number of
                 experiments are performed with the GP classifiers
                 achieving good performance based on standard
                 performance metrics. Moreover, a proof-of-concept
                 real-world prototype is presented, where a GP
                 classifier is transferred and hard-coded on an embedded
                 system using a digital-to-analogue converter and a
                 field programmable gate array, achieving a low average
                 classification error of 14.55percent, sensitivity
                 values between 0.65 and 0.97, and specificity values
                 between 0.86 and 0.94.

                 Conclusions: The proposed approach achieves good
                 results for stage identification, particularly when
                 compared with previous works that focus on this task.
                 The results show that the problem of intra-class
                 classification can be solved with a low error, and high
                 sensitivity and specificity. Moreover, the limitations
                 of the approach are identified and good operating
                 configurations can be proposed based on the results.",
}

Genetic Programming entries for Arturo Sotelo Enrique Guijarro Estelles Leonardo Trujillo Luis N Coria Yuliana Martinez

Citations