EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis
Автор: Mahmoud I. Kamel, Mohammed J. Alhaddad, Hussein M. Malibary, Khalid Thabit, Foud Dahlwi, Ebtehal A. Alsaggaf, Anas A. Hadi
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 3 vol.4, 2012 года.
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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.
Electroencephalogram, Automated diagnosis, Autism, Regularized Fisher's linear discriminant analysis, Fast Fourier Transform
Короткий адрес: https://sciup.org/15012250
IDR: 15012250
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