Basic and Clinical Neuroscience Journal
مجله علوم اعصاب پایه و بالینی
BCN
Medical Sciences
http://bcn.iums.ac.ir
137
journal137
2008-126X
2228-7442
10.32598/bcn
en
jalali
1400
4
1
gregorian
2021
7
1
12
4
online
1
fulltext
en
EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach
Computational Neuroscience
Computational Neuroscience
Original
Original
<strong>Introduction</strong>: 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.<br>
<strong>Methods</strong>: 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. <br>
<strong>Results</strong>: 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.<br>
<strong>Conclusion</strong>: 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.
EEG, Wavelets, Artifacts, Empirical Mode Decomposition (EMD), Depression
465
476
http://bcn.iums.ac.ir/browse.php?a_code=A-10-1388-2&slc_lang=en&sid=1
Chamandeep
Kaur
ckchauhan.86@gmail.com
13700319475328460037950
13700319475328460037950
No
Department of Electronics and Communication Engineering, Panjab University Chandigarh, Chandigarh, India.
Preeti
Singh
preeti_singh@pu.ac.in
13700319475328460037951
13700319475328460037951
Yes
Department of Electronics and Communication Engineering, Panjab University Chandigarh, Chandigarh, India.
Sukhtej
Sahni
13700319475328460037952
13700319475328460037952
No
Department of Psychiatry, Cheema Medical Complex, Mohali, India.