A Framework for Epilepsy Classification Using Modified Sparse Representation Classifiers and Naïve Bayesian Classifier from Electroencephalogram Signals
Author:
Publisher
American Scientific Publishers
Subject
Health Informatics,Radiology Nuclear Medicine and imaging
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Performance comparison of bio-inspired and learning-based clustering analysis with machine learning techniques for classification of EEG signals;Frontiers in Artificial Intelligence;2023-06-21
2. Fuzzy C-Means Clustering and Gaussian Mixture Model for Epilepsy Classification from EEG;Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB);2019
3. Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis (Erratum);Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2017;2017-08-07
4. Multilinear Spatial Discriminant Analysis for Dimensionality Reduction;IEEE Transactions on Image Processing;2017-06
5. Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed;Multimedia Tools and Applications;2017-03-13
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