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EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach
INTRODUCTION: Several computer-aided diagnosis systems for depression are suggested for use by clinicians to authorize the diagnosis. EEG may be used as an objective analysis tool for identifying depression in the initial stage to avoid it from reaching a severe and permanent state. However, artifac...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Iranian Neuroscience Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817173/ https://www.ncbi.nlm.nih.gov/pubmed/35154587 http://dx.doi.org/10.32598/bcn.2021.1388.2 |
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author | Kaur, Chamandeep Singh, Preeti Sahni, Sukhtej |
author_facet | Kaur, Chamandeep Singh, Preeti Sahni, Sukhtej |
author_sort | Kaur, Chamandeep |
collection | PubMed |
description | INTRODUCTION: Several computer-aided diagnosis systems for depression are suggested for use by clinicians to authorize the diagnosis. EEG may be used as an objective analysis tool for identifying depression in the initial stage to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. METHODS: This work proposes a novel denoising method based on Empirical Mode Decomposition (EMD) (with Detrended Fluctuation Analysis (DFA) and wavelet packet transform. Initially, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further Wavelet Packets Decomposition (WPD)-based evaluation is applied to extract the cleaner signal. RESULTS: Simulations were conducted on real EEG databases for depression to demonstrate the effects of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE were identified. The obtained results indicated improved signal-to-noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Additionally, Random Forest and SVM (Support Vector Machine)-based classification revealed the improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique equaled 98.0% and 97.21% for the EMD- DFA technique for RF and SVM, respectively, compared to the proposed method. Furthermore, the classification performance for both classifiers was compared with and without denoising to highlight the effects of the proposed technique. CONCLUSION: Proposed denoising system results in better classification of depressed and healthy individuals resulting in a better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of the EMD approach. |
format | Online Article Text |
id | pubmed-8817173 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Iranian Neuroscience Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-88171732022-02-10 EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach Kaur, Chamandeep Singh, Preeti Sahni, Sukhtej Basic Clin Neurosci Research Paper INTRODUCTION: Several computer-aided diagnosis systems for depression are suggested for use by clinicians to authorize the diagnosis. EEG may be used as an objective analysis tool for identifying depression in the initial stage to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. METHODS: This work proposes a novel denoising method based on Empirical Mode Decomposition (EMD) (with Detrended Fluctuation Analysis (DFA) and wavelet packet transform. Initially, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further Wavelet Packets Decomposition (WPD)-based evaluation is applied to extract the cleaner signal. RESULTS: Simulations were conducted on real EEG databases for depression to demonstrate the effects of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE were identified. The obtained results indicated improved signal-to-noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Additionally, Random Forest and SVM (Support Vector Machine)-based classification revealed the improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique equaled 98.0% and 97.21% for the EMD- DFA technique for RF and SVM, respectively, compared to the proposed method. Furthermore, the classification performance for both classifiers was compared with and without denoising to highlight the effects of the proposed technique. CONCLUSION: Proposed denoising system results in better classification of depressed and healthy individuals resulting in a better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of the EMD approach. Iranian Neuroscience Society 2021 2021-07-01 /pmc/articles/PMC8817173/ /pubmed/35154587 http://dx.doi.org/10.32598/bcn.2021.1388.2 Text en Copyright© 2021 Iranian Neuroscience Society https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) |
spellingShingle | Research Paper Kaur, Chamandeep Singh, Preeti Sahni, Sukhtej EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach |
title | EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach |
title_full | EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach |
title_fullStr | EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach |
title_full_unstemmed | EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach |
title_short | EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach |
title_sort | eeg artifact removal system for depression using a hybrid denoising approach |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817173/ https://www.ncbi.nlm.nih.gov/pubmed/35154587 http://dx.doi.org/10.32598/bcn.2021.1388.2 |
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