An EMD and IMF Energy Entropy Based Optimized Feature Extraction and Classification Scheme for Single Trial EEG Signal

Abstract For the purpose of improving the classification accuracy of single trial EEG signal during motor imagery (MI) process, this study proposed a classification method which combined IMF energy entropy and improved EMD scheme. Singular value decomposition (SVD), Gaussian mixture model, EMD and IMF energy entropy were employed for the newly designed scheme. After removing noise and artifacts from acquired EEG signals in EEGLAB, SVD was applied, and the singular values were clustered by Gaussian mixture model. The insignificant characteristics indicated by the small SVD values were then removed, and the signals were reconstructed, feeding to EMD algorithm. Those IMFs mapping to δ、θ、α and β frequencies were selected as the major features of the EEG signal. The SVM classifier with RBF, linear, and polynomial kernel functions and voting mechanism then kicked in for classification. The results were compared with that of the traditional EMD and EEMD through simulation, showing that the proposed scheme can eliminate mode mixing effectively and improve the single trial EEG signal classification accuracy significantly, suggesting the probability of designing a more efficient EEG control system based on the proposed scheme.

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Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2176104 ◽

2021 ◽

pp. 418-426

Author(s):

Suhas S ◽

Dr. C. R. Venugopal

Keyword(s):

Support Vector Machine ◽

Image Retrieval ◽

Classification Accuracy ◽

Kernel Functions ◽

Polynomial Kernel ◽

Support Vector ◽

Svm Classifier ◽

Mr Images ◽

Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

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Gaussian Mixture Model Based Hierarchical Clustering in Prediction of Autism Spectrum Disorder

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a1322.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 5751-5756

Keyword(s):

Autism Spectrum Disorder ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Hierarchical Clustering ◽

Classification Accuracy ◽

Gaussian Mixture ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Model Based ◽

Proper Diagnosis

Autism is one of the most complex and divergent class disorders which accompany various lacking in symptoms needed for classification, societal interaction, abridged verbal communication, and monotonous behavior. Timely and proper diagnosis of Autism Spectrum Disorder can ensure the offering of medical treatment and guidance to get cure. In this paper, Gaussian Mixture Model based Hierarchical Clustering is proposed for efficiently predicting the Autism Spectrum Disorder. Also, Flexible splitting concept was proposed for hierarchical clustering in order to increase the quality of guessing and classification accuracy. The proposed algorithm is validated to check the performance against the existing method. The results shows that the proposed algorithm outperforms the existing algorithm in terms of classification accuracy.

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