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ADASYN and ABC-optimized RBF convergence network for classification of electroencephalograph signal
, S. Mishra, P.K. Mallick, G.-S. Chae
Published in Springer Science and Business Media Deutschland GmbH
Electroencephalograph (EEG) is supposed to be a major challenge in the area of biomedical signal processing. Being one of the widely used invasive techniques, it is capable to find many cases of brain disorder problems like epileptic seizures and sleep disorder. This work follows the procedure of convergence computing where there are different computing techniques have been combined together to achieve our final goal with much perfection. radial basis function (RBF) being one of the simplest forms of neural network (NN) can be used for the purpose of EEG signal classification, where the network uses the radial basis function as an activation function. In this study, artificial bee colony (ABC) technique was applied for optimizing the parameters that were required in the RBF network for the EEG signal classification. Adaptive synthetic oversampling (ADASYN) process was considered for improving the learning method of managing the class imbalance problem in the EEG dataset. An EEG dataset for normal and epileptic person is typically seen and is processed where five datasets are combined together in three different ways. The new datasets were grouped and were created as set 1 (A+D & E), set 2 (B+D & E), and set 3 (C+D & E). The ABC optimized RBF classifier is applied along with ADASYN method where the resolution for the above stated datasets states that the classification accuracy for set 1 and set 3 were increasing significantly as compared to the standard RBF classifier network. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
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JournalData powered by TypesetPersonal and Ubiquitous Computing
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH