Research Article
Performance Evaluation of Different Threshold Estimation Rules in Denoising EEG Singal with Hamming Window-Based Shrinkage Technique
Issue:
Volume 10, Issue 4, December 2025
Pages:
168-174
Received:
12 August 2025
Accepted:
13 September 2025
Published:
10 October 2025
DOI:
10.11648/j.ajset.20251004.11
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Abstract: Signal denoising is an integral part of contaminated signal processing to obtain the signal of interest. In this research, a developed system for reliable removal of powerline interference from Electroencephalographic (EEG) signal based on descrete wavelet transform technique is designed which includes soft thresholding –based shrinkage function called hamming window (Ham-WSST). A practical EEG signal was acquired by measurement from Federal Medical Centre, Owerri and contaminated with powerline noise of 50Hz. This was sampled at a frequency of 1000Hz. Due to the new shrinkage function, decomposition level of 7 and daubechies 7 (db7) mother wavelet, the denoising of the powerline noise was extensively performed in combination with Sqtwolog, Rigrsure, Heursure and Minimaxi rule. The outcome results for the four threshold rules of the system were evaluated and compared using power spectral density (PSD), signal to noise (SNR), mean square error (MSE) and maximum absolute error (MAE) estimation functions. The power spectral density result established on the optimum decomposition level of 7 at 0.1 radian normalised frequency was 35.89 dB for Sqtwolog rule, 37.68dB for Rigrsure, 37.68dB for Heursure and for Minimaxi value is 36.52dB. For signal to noise ratio (SNR) the value for Sqwolog is 42.26 dB, Rigrsure is 38.68dB, Heursure is 38.68dB and Minimaxi is 40.55dB. The estimation values for mean square error (MSE) and maximum absolute error (MAE) for Sqtwolog rule, Rigrsure rule, Heursure rule and Minimaxi rule in this order is giving as 0.00147, 0.0046, 0.00492, and 0.00206; 0.1147, 0.1245, 0.1245 and 0.1158. Less PSD value means more noise attenuation at the considered frequency instant, higher value for SNR indicate more signal of interest than noise while lower values of MSE and MAE indicate less error. The research further shows that the window thresholding shrinkage function based on the Hamming window with the Sqtwolog estimation rule is more effective at denoising contaminated EEG signals with powerline noise..
Abstract: Signal denoising is an integral part of contaminated signal processing to obtain the signal of interest. In this research, a developed system for reliable removal of powerline interference from Electroencephalographic (EEG) signal based on descrete wavelet transform technique is designed which includes soft thresholding –based shrinkage function ca...
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