Analysis and Comparison of Artifact Removal Techniques for Epilepsy EEG Signal

G N Mamatha  lowast; S A Hariprasad

doi:10.17485/IJST/v17i23.3

Article

Analysis and Comparison of Artifact Removal Techniques for Epilepsy EEG Signal

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i23.3

Year: 2024, Volume: 17, Issue: 23, Pages: 2370-2380

Original Article

Analysis and Comparison of Artifact Removal Techniques for Epilepsy EEG Signal

G N Mamatha^1∗, S A Hariprasad²

¹Department of Electronics and Communication, Faculty of Engineering and Technology, Jain University, Jain Global Campus, Jakkasandra Post, Kanakapura Main Road, Ramanagara, Bengaluru, Karnataka, India
²Department of Electronics Engineering, Jain (Deemed-to-be University), Bengaluru, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:02 January 2024, Accepted Date:06 May 2024, Published Date:03 June 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: Accurate epilepsy diagnosis demands precise EEG analysis, hindered by non-neuronal artifacts. This study evaluates artifact removal methods, specifically Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD), aiming to enhance signal quality. We introduce a hybrid approach, combining ICA and EMD. Methods: ICA and EMD are applied to preprocess epilepsy EEG recordings. Quantitative evaluation metrics, including Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Standard Deviation (SD), are calculated and compared for both methods. Findings: ICA outperforms EMD, showing higher SNR and PSNR, notably in BONN and CHB-MIT datasets. ICA achieves significant reductions in MSE, RMSE, and SD. The hybrid approach surpasses existing methods, supported by quantitative data. Novelty: Rigorous application of ICA and EMD to diverse datasets quantitatively establishes ICA's superiority. The hybrid approach, backed by quantitative evidence, proves effective beyond epilepsy EEG. Conclusion: This abstract provides clear, quantitative support for ICA's superiority and the hybrid approach's efficacy, offering valuable insights into artifact removal in EEG analysis.

Keywords: Epilepsy, Artifact removal, EEG, ICA, DWT, EMD, Performance metrics

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Copyright

© 2024 Mamatha & Hariprasad. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)