Classification of Parkinson's disease patients based on spectrogram using local binary pattern descriptors
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Fecha
2022
Autores
Gelvez-Almeida, E
Váasquez-Coronel, A
Guatelli, R
Aubin, V
Mora, M
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Editor
IOP Publishing
Resumen
Extreme learning machine is an algorithm that has shown a good performance
facing classi cation and regression problems. It has gained great acceptance by the scienti c
community due to the simplicity of the model and its sola great generalization capacity.
This work proposes the use of extreme learning machine neural networks to carry out the
classi cation between Parkinson's disease patients and healthy individuals. The descriptor used
corresponds to the feature vector generated applying the local binary Pattern algorithm to the
grayscale spectrograms. The spectrograms are obtained from the audio signal samples from
the considered repository. Experiments are conducted with single hidden layer and multilayer
extreme learning machine networks comparing the results of each structure. Results show that
hierarchical extreme learning machine with three hidden layers has a better general performance
over multilayer extreme learning machine networks and a single hidden layer extreme learning
machine. The rate of success obtained is within the ranges presented in the literature. However,
the hierarchical network training time is considerably faster compared to multilayer networks
of three or two hidden layers.
Descripción
Palabras clave
Parkinson's disease patients, Local binary pattern descriptors, Extreme learning machine
Citación
E Gelvez-Almeida et al 2022 J. Phys.: Conf. Ser. 2153 012014