Mahmoud I. Kamel; Mohammed J. Alhaddad; Hussein M. Malibary; Khalid Thabit; Foud Dahlwi; Ebtehal A. Alsaggaf; Anas A. Hadi

EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis

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Author(s)

Mahmoud I. Kamel ^1,* Mohammed J. Alhaddad ¹ Hussein M. Malibary ¹ Khalid Thabit ¹ Foud Dahlwi ¹ Ebtehal A. Alsaggaf ¹ Anas A. Hadi ¹

1. Faculty of Computing and Information Technology King Abdulaziz University KAU, Jeddah, Saudi Arabia

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2012.03.06

Received: 11 Jan. 2012 / Revised: 15 Feb. 2012 / Accepted: 21 Mar. 2012 / Published: 8 Apr. 2012

Index Terms

Electroencephalogram, Automated diagnosis, Autism, Regularized Fisher's linear discriminant analysis, Fast Fourier Transform

Abstract

Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG) analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT) with the Regulated Fisher Linear Discriminant (RFLD) classifier.
Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD) has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.

Cite This Paper

Mahmoud I. Kamel , Mohammed J. Alhaddad, Hussein M. Malibary, Khalid Thabit, Foud Dahlwi, Ebtehal A. Alsaggaf, Anas A. Hadi,"EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis", IJIGSP, vol.4, no.3, pp.35-41, 2012. DOI: 10.5815/ijigsp.2012.03.06

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