Influence of wavelet boundary conditions on the classification of biological signals

Author

Gutierrez, Angel ; Somolinos, Mlfredo

Author_Institution

Dept. of Comput. Sci., Montclair State Univ., NJ, USA

fYear

2000

fDate

2000

Firstpage

25

Lastpage

26

Abstract

Doctors utilized Brainstem Auditory Evoked Potentials (BAEP) to diagnose patients with multiple sclerosis. We use eight coefficients of each of the several wavelet transforms of the BAEP signals to train an artificial neural network with radial basis functions. We study how the boundary conditions used to determine the wavelet transforms affect the maximum number of correct diagnoses. Using this information, we establish the best strategy to avoid misleading information created by the boundary conditions

Keywords

auditory evoked potentials; medical signal processing; radial basis function networks; signal classification; wavelet transforms; Coiflets; Haar wavelet; artificial neural network; biological signals classification; boundary conditions; brainstem auditory evoked potentials; multiple sclerosis patients; radial basis functions; smooth padding; wavelet boundary conditions; zero padding; Boundary conditions; Computer science; Educational institutions; Frequency; Interpolation; Mathematics; Multiple sclerosis; Neural networks; Spline; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioengineering Conference, 2000. Proceedings of the IEEE 26th Annual Northeast

Conference_Location

Storrs, CT

Print_ISBN

0-7803-6341-8

Type

conf

DOI

10.1109/NEBC.2000.842361

Filename

842361