Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform
| Autor: | Md. Ekramul Hamid, Md. Khademul Islam Molla, Md. Sujan Ali, Mst. Jannatul Ferdous |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Physics::Medical Physics lcsh:TK7800-8360 02 engineering and technology Electroencephalography Signal lcsh:QA75.5-76.95 03 medical and health sciences 0302 clinical medicine Wavelet Artificial Intelligence Band selection 0202 electrical engineering electronic engineering information engineering medicine medicine.diagnostic_test Quantitative Biology::Neurons and Cognition business.industry lcsh:Electronics Wavelet transform Pattern recognition Computer Science Applications Artifact suppression 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electronic computers. Computer science business 030217 neurology & neurosurgery Software |
| Zdroj: | International Journal of Advanced Robotic Systems, Vol 18 (2021) |
| ISSN: | 1729-8814 |
| Popis: | This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost. |
| Databáze: | OpenAIRE |
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