Soft Computing Techniques for Advanced Epileptic EEG Analysis and Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{LingGuo:thesis,
  author =       "Ling Guo",
  title =        "Soft Computing Techniques for Advanced Epileptic EEG
                 Analysis and Classification",
  school =       "Facultade de Informatica, Universidade da Coruna",
  year =         "2011",
  address =      "Spain",
  month =        "26 " # may,
  keywords =     "genetic algorithms, genetic programming, inteligencia
                 artificial",
  URL =          "http://www.fic.udc.es/NewsContent.do?newsId=25045&urlCurrent=ViewPaginatedCategory.do",
  broken =       "http://tesis.com.es/documentos/soft-computing-techniques-for-advanced-epileptic-eeg-analysis-and-classification/",
  size =         "210 pages",
  abstract =     "Epilepsy is an abnormal neurological status that makes
                 people susceptible to brief electrical disturbance in
                 the brain thus producing a change in sensation,
                 awareness, and/or behaviour and is characterised by
                 recurrent seizures. It affects up to 1percent of the
                 population in the world. Two-thirds of the epileptic
                 patients can be treated through medications. Another
                 8percent may benefit from surgery. But 25percent of
                 people with epilepsy continue to have seizures and no
                 treatment suits them.

                 Electroencephalogram (EEG) is the recording of
                 electrical activity of the brain and it contains much
                 valuable information for understanding epilepsy. In
                 clinic environments, the neurologists have to
                 continuously observe the EEG recordings for better
                 understanding epilepsy, which is time-consuming and
                 tedious. Thus, efforts on developing automatic
                 epileptic seizure detection on EEG background are of
                 great importance for epilepsy diagnosis and treatment,
                 and to improve the clinical assistance and, at last,
                 for enhancing the whole health system.

                 This research successfully combines soft computing
                 techniques of Artificial Neural Networks (ANNs) and
                 Genetic Programming (GP) with signal processing tools
                 of wavelet transform and multiwavelet analysis for
                 advanced epileptic EEG signal analysis and
                 classification. The main objectives of this
                 dissertation are specifically:

                 on scalar wavelet processing technique. Scalar wavelets
                 are efficient in non-stationary signal analysis.
                 Amounts of classical features based on wavelet analysis
                 have been used for EEG classification by many
                 researches. In this study, new features as Relative
                 Wavelet Energy (RWE) and Line Length (LL) are
                 introduced and extracted from wavelet decomposed EEG
                 signals. Combing these new extracted features with ANNs
                 aims to distinguish epileptic and nonepileptic EEG
                 recordings.",
  abstract =     "Proposing a novel detection method based on
                 multiwavelet processing technique. Multi-wavelets are
                 parts of wavelet theory, however, they have some
                 difference comparing with scalar wavelets. It is usual
                 to apply scalar wavelets to analyse EEG signals, while,
                 using multiwavelets to process EEG signals is an
                 untapped research filed. In this proposed method,
                 multiwavelet transform analyses EEG signals through
                 decomposing the signal into several narrow frequency
                 bands. Then, features sensitive in detecting epileptic
                 activity are extracted from the decomposed signals.
                 Finally, the seizure detection procedure is completed
                 through inputting the extracted feature space into a
                 classifier system. Additionally, comparisons between
                 multiwavelets and scalar wavelets on EEG signal
                 analysis are also investigated in this method.

                 Applying GP to perform automatic feature extraction.
                 The purpose of this study is to improve the performance
                 of KNN classifier and reduce the input feature
                 dimensionality simultaneously. GP is used to create
                 GP-based features from a set of classical features for
                 detecting epileptic seizures. GP is an automated
                 routine in the family of Evolutionary Computation (EC)
                 that can be used to generate optimal, artificial
                 features. The features are optimal in the sense that
                 the heuristics of EC maximise an objective function,
                 which measures the performance of the artificial
                 features in distinguishing epileptic activity from
                 non-epileptic activity. The obtained GP-based features
                 are thought to be artificial because GP returns
                 computer-crafted results that might not have physical
                 meanings.

                 Over the years, it has become increasingly clear that
                 the areas of neuroscience,soft computing techniques and
                 EEG signal analysis are not mutually exclusive research
                 areas. Rather, they represent different aspects and any
                 new knowledge gained from one may be a stepping stone
                 for the others. Hopefully, the results of this research
                 can lead to a better diagnosis and treatment of
                 epileptic seizures and an improved quality of life for
                 the millions of persons affected by epilepsy.",
  abstract =     "INTRODUCCION

                 La epilepsia es un estado neurologico anormal provocado
                 por una perturbacion electrica anomala y breve en una
                 zona del cerebro, lo que produce un cambio en la
                 sensacion, la conciencia y el comportamiento, y se
                 caracteriza por convulsiones recurrentes. Afecta al
                 1percent de la poblacion en todo el mundo. Dos tercios
                 de los pacientes epilepticos pueden ser tratados con
                 medicamentos, mientras que otro 8percent se pueden
                 beneficiar de la cirugia. Sin embargo, el 25percent de
                 las personas con epilepsia seguiran teniendo
                 convulsiones y no podran ser tratadas.

                 El Electroencefalograma (EEG) es el registro de la
                 actividad electrica del cerebro y contiene mucha
                 informacion valiosa para la comprension de esta
                 enfermedad. En los entornos clinicos, los neurologos
                 han de observar continuamente el EEG para comprender
                 mejor la epilepsia, proceso que es largo y tedioso. Por
                 lo tanto, los esfuerzos para el desarrollo de sistemas
                 de deteccion automatica de ataques epilepticos mediante
                 el analisis de las senales de EEG son de gran
                 importancia para el diagnostico de la epilepsia y su
                 tratamiento.

                 Esta investigacion combina tecnicas de Soft Computing
                 como Redes Neuronales Artificiales (RR.NN.AA.) y
                 Programacion Genetica (PG) con herramientas de
                 procesamiento de senal, transformada wavelet y
                 multiwavelet, para realizar un analisis avanzado y
                 clasificacion de la senal de EEG relacionada con la
                 enfermedad de epilepsia.

                 Concretamente, los principales objetivos de esta Tesis
                 son:

                 Desarrollar un modelo para la deteccion de crisis
                 epilepticas a traves de la extraccion de nuevas
                 caracteristicas basadas en el analisis escalar wavelet.
                 En este estudio, este analisis se utiliza para extraer
                 nuevas caracteristicas con el objetivo de clasificar
                 las senales de EEG. Estas nuevas caracteristicas se
                 combinan con RR.NN.AA. con el objetivo de distinguir
                 entre grabaciones de EEG epilepticos y no
                 epilepticos.

                 Proponer un nuevo metodo de deteccion basada en la
                 tecnica de procesamiento multiwavelet. Esta es parte de
                 la teoria de wavelets, sin embargo, tienen alguna
                 diferencia en comparacion con los wavelets escalares.
                 La aplicacion de wavelets escalares para analizar las
                 senales de EEG es una tarea habitual, mientras que el
                 uso de multiwavelets para procesar las senales de EEG
                 es un campo de investigacion apenas sin
                 explotar.

                 Aplicar PG para realizar la extraccion automatica de
                 caracteristicas. El proposito de este estudio es
                 mejorar los resultados ofrecidos por algoritmo de
                 clasificacion del vecino mas cercano (K-Nearest
                 Neighbor, KNN) y reducir la dimensionalidad del
                 conjunto de entrada simultaneamente.",
  abstract =     "METODOLOGIA

                 Para conseguir estos objetivos, en este trabajo se
                 propone el uso de una metodologia que permita aplicar
                 dichas tecnicas de la siguiente manera:

                 En el primer metodo, las senales de EEG en primer lugar
                 se descomponen mediante la transformada wavelet
                 discreta en varias senales, y de cada una de ellas se
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