ENIC: Ensemble and Nature Inclined Classification with Sparse Depiction based Deep and Transfer Learning for Biosignal Classification

The electrical activities of the brain are recorded and measured with Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain. It is quite a famous and versatile methodology utilized in both clinical and academic research activities. In this work, sparse depiction i...

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Vydáno v:Applied soft computing Ročník 117; s. 108416
Hlavní autoři: Prabhakar, Sunil Kumar, Lee, Seong-Whan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.03.2022
Témata:
Deep learning
EEG
ELM
K-SVD
SOM
Sparse
Swarm intelligence
Transfer learning
SOM
Deep learning
Transfer learning
EEG
ELM
K-SVD
Sparse
Swarm intelligence
ISSN:1568-4946, 1872-9681
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Shrnutí:The electrical activities of the brain are recorded and measured with Electroencephalography (EEG) by means of placing the electrodes on the scalp of the brain. It is quite a famous and versatile methodology utilized in both clinical and academic research activities. In this work, sparse depiction is initially incorporated to the EEG signals by means of using K-Singular Value Decomposition (K-SVD) algorithm and the features are extracted by means of using Self-Organizing Map (SOM) technique. The extracted features are initially classified with Extreme Learning Machine (ELM) and the proposed classification versions of ELM such as Ensemble ELM model and Nature Inclined ELM Model. The proposed ensemble ELM model makes use of the combination of Modified Adaboost. RT based on wavelet thresholding with ELM. The proposed Nature Inclined ELM makes use of the combination of some famous swarm intelligence algorithms such as Genetic Algorithm based ELM (GA-ELM), Particle Swarm Optimization based ELM (PSO-ELM), Ant Colony Optimization based ELM (ACO-ELM), Artificial Bee Colony based ELM (ABC-ELM) and Glowworm Swarm Optimization based ELM (GSO-ELM). The​ extracted features are also classified with deep learning methodology by means of utilizing an incidental Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). Another famous methodology using Non-negative Matrix Factorization (NMF) and Affinity Propagation Congregation based Mutual Information (APCMI) with transfer learning techniques is also proposed and implemented once the sparse modelling is done and the results are analysed. The proposed methodology is implemented for two EEG datasets such as epilepsy dataset and schizophrenia dataset and a comprehensive analysis is done with very promising results. •Sparse Depiction is imposed to EEG Signals initially.•K-SVD algorithm and Self-Organizing Map technique is utilized.•An Ensemble and Nature Inclined Classification model with ELM is proposed.•A transfer learning model along with deep learning model is also implemented.•The developed models are tested on two EEG signal datasets.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2022.108416