Three Dimensional Analysis of SPECT Images for Diagnosing Early Parkinson’s Disease using Radial Basis Function Kernel − Extreme Learning Machine

2019 ◽  
Vol 15 (5) ◽  
pp. 461-470
Author(s):  
Sebasthiyar Anita ◽  
Panchnathan Aruna Priya
Keyword(s):  
Parkinson’S Disease ◽  
Radial Basis Function ◽  
Extreme Learning Machine ◽  
Basis Function ◽  
Three Dimensional ◽  
Threshold Level ◽  
Radial Basis Function Kernel ◽  
Radial Basis ◽  
Specific Uptake ◽  
Learning Machine

Background: Parkinson’s Disease (PD) is caused by the deficiency of dopamine, the neurotransmitter that has an effect on specific uptake region of the substantia nigra. Identification of PD is quite tough at an early stage. Objective: The present work proposes an expert system for three dimensional Single-Photon Emission Computed Tomography (SPECT) image to diagnose the early PD. Methods: The transaxial image slices are selected on the basis of their high specific uptake region. The processing techniques like preprocessing, segmentation and feature extraction are implemented to extract the quantification parameters like Intensity, correlation, entropy, skewness and kurtosis of the images. The Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers using Radial Basis Function kernel (RBF) are implemented and their results are compared in order to achieve better performance of the system. The performance of the system is evaluated in terms of sensitivity, specificity analysis, accuracy, Receiver Operating Curve (ROC) and Area Under the Curve (AUC). Results: It is found that RBF-ELM provides high accuracy of 98.2% in diagnosing early PD. In addition, the similarity among the features is found out using K-means clustering algorithm to compute the threshold level for early PD. The computed threshold level is validated using Analysis of Variance (ANOVA). Conclusion: The proposed system has a great potential to assist the clinicians in the early diagnosis process of PD.

scholarly journals Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Beomjun Min ◽  
Jongin Kim ◽  
Hyeong-jun Park ◽  
Boreom Lee
Keyword(s):  
Radial Basis Function ◽  
Extreme Learning Machine ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Single Trial ◽  
Eeg Data ◽  
Radial Basis ◽  
Learning Machine ◽  
Silent Speech

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems.

MELM-GRBF: A modified version of the extreme learning machine for generalized radial basis function neural networks

2011 ◽  
Vol 74 (16) ◽  
pp. 2502-2510 ◽  
Author(s):  
Francisco Fernández-Navarro ◽  
César Hervás-Martínez ◽  
Javier Sanchez-Monedero ◽  
Pedro Antonio Gutiérrez
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Extreme Learning Machine ◽  
Basis Function ◽  
Radial Basis ◽  
Learning Machine

scholarly journals Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Jungshin Lee ◽  
Changky Sung ◽  
Juhyun Oh
Keyword(s):  
Radial Basis Function ◽  
Extreme Learning Machine ◽  
Basis Function ◽  
Bilinear Interpolation ◽  
Terrain Referenced Navigation ◽  
Digital Elevation ◽  
Large Memory ◽  
Radial Basis ◽  
Learning Machine ◽  
Elevation Model

A high-resolution digital elevation model (DEM) is an important element that determines the performance of terrain referenced navigation (TRN). However, the higher the resolution of the DEM, the bigger the memory size needed for storing it. It is difficult to secure such large memory spaces in small, low-priced unmanned aerial vehicles. In this study, a high-precision terrain regression model to fit the DEM is generated using the extreme learning machine technique based on the multilayer radial basis function. The TRN results using the proposed method are compared with existing studies on various DEM fitting methods. This study verifies that the proposed method obtains improved fitting accuracy and TRN performance over existing DEM fitting methods such as bilinear interpolation, SVM for regression, and bi-spline neural network, without the DEM storage space.

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