Open AccessClassification of Parkinson’s disease patients based on spectrogram using local binary pattern descriptors
Author(s) -
E Gelvez-Almeida,
A Váasquez-Coronel,
Renata Guatelli,
Verónica I. Aubin,
Marco Mora
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2153/1/012014
Subject(s) - extreme learning machine , spectrogram , artificial intelligence , computer science , support vector machine , pattern recognition (psychology) , feature (linguistics) , machine learning , binary classification , generalization , binary number , artificial neural network , mathematics , mathematical analysis , linguistics , philosophy , arithmetic
Extreme learning machine is an algorithm that has shown a good performance facing classification and regression problems. It has gained great acceptance by the scientific 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 classification 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.