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...

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Autores principales:	Chamandeep Kaur, Preeti Singh, Sukhtej Sahni
Formato:	article
Lenguaje:	EN
Publicado:	Iran University of Medical Sciences 2021
Materias:	eeg wavelets artifacts empirical mode decomposition (emd) depression Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/0a4704bf84c14384b14afa58f1ec9aee
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spelling	oai:doaj.org-article:0a4704bf84c14384b14afa58f1ec9aee2021-12-05T08:43:44ZEEG Artifact Removal System for Depression Using a Hybrid Denoising Approach2008-126X2228-7442https://doaj.org/article/0a4704bf84c14384b14afa58f1ec9aee2021-07-01T00:00:00Zhttp://bcn.iums.ac.ir/article-1-1468-en.htmlhttps://doaj.org/toc/2008-126Xhttps://doaj.org/toc/2228-7442Introduction: 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.Chamandeep KaurPreeti SinghSukhtej SahniIran University of Medical Sciencesarticleeegwaveletsartifactsempirical mode decomposition (emd)depressionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBasic and Clinical Neuroscience, Vol 12, Iss 4, Pp 465-476 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	eeg wavelets artifacts empirical mode decomposition (emd) depression Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	eeg wavelets artifacts empirical mode decomposition (emd) depression Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Chamandeep Kaur Preeti Singh Sukhtej Sahni EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach
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	article
author	Chamandeep Kaur Preeti Singh Sukhtej Sahni
author_facet	Chamandeep Kaur Preeti Singh Sukhtej Sahni
author_sort	Chamandeep Kaur
title	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_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_sort	eeg artifact removal system for depression using a hybrid denoising approach
publisher	Iran University of Medical Sciences
publishDate	2021
url	https://doaj.org/article/0a4704bf84c14384b14afa58f1ec9aee
work_keys_str_mv	AT chamandeepkaur eegartifactremovalsystemfordepressionusingahybriddenoisingapproach AT preetisingh eegartifactremovalsystemfordepressionusingahybriddenoisingapproach AT sukhtejsahni eegartifactremovalsystemfordepressionusingahybriddenoisingapproach
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EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach

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