es | en | pt | fr
    • Presentación
    • Países
    • Instituciones
    • Participa
        JavaScript is disabled for your browser. Some features of this site may not work without it.
        Ver ítem 
        •   Inicio
        • Colombia
        • Universidades
        • Universidad Simón Bolívar (Colombia)
        • Ver ítem
        •   Inicio
        • Colombia
        • Universidades
        • Universidad Simón Bolívar (Colombia)
        • Ver ítem

        Classification of Parkinson's disease patients based on spectrogram using local binary pattern descriptors

        Fecha
        2022
        Registro en:
        E Gelvez-Almeida et al 2022 J. Phys.: Conf. Ser. 2153 012014
        17426596
        https://hdl.handle.net/20.500.12442/13159
        https://doi.org/10.1088/1742-6596/2153/1/012014
        https://repositorioslatinoamericanos.uchile.cl/handle/2250/8357133
        Autor
        Gelvez-Almeida, E
        Váasquez-Coronel, A
        Guatelli, R
        Aubin, V
        Mora, M
        Institución
        • Universidad Simón Bolívar (Colombia)
        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.
        Materias
        Parkinson's disease patients
        Local binary pattern descriptors
        Extreme learning machine

        Mostrar el registro completo del ítem


        Red de Repositorios Latinoamericanos
        + de 8.000.000 publicaciones disponibles
        500 instituciones participantes
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Ingreso Administradores
        Colecciones destacadas
        • Tesis latinoamericanas
        • Tesis argentinas
        • Tesis chilenas
        • Tesis peruanas
        Nuevas incorporaciones
        • Argentina
        • Brasil
        • Colombia
        • México
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Red de Repositorios Latinoamericanos | 2006-2018
         

        EXPLORAR POR

        Instituciones
        Fecha2011 - 20202001 - 20101951 - 20001901 - 19501800 - 1900

        Explorar en Red de Repositorios

        Países >
        Tipo de documento >
        Fecha de publicación >
        Instituciones >

        Red de Repositorios Latinoamericanos
        + de 8.000.000 publicaciones disponibles
        500 instituciones participantes
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Ingreso Administradores
        Colecciones destacadas
        • Tesis latinoamericanas
        • Tesis argentinas
        • Tesis chilenas
        • Tesis peruanas
        Nuevas incorporaciones
        • Argentina
        • Brasil
        • Colombia
        • México
        Dirección de Servicios de Información y Bibliotecas (SISIB)
        Universidad de Chile
        Red de Repositorios Latinoamericanos | 2006-2018