Different discrete wavelet functions are optimal for denoising EEG signals depending on whether the subject is healthy (db8) or epileptic (orthogonal Meyer).
Subject-specific wavelet selection may optimize EEG denoising; leaves open prospective validation for clinical epilepsy monitoring.
Analyzing Electroencephalogram (EEG) signal is a challenge due to the various artifacts used by Electromyogram,eye blink and Electrooculogram. The present de-noising techniques that are based on the frequency selective filteringsuffers from a substantial loss of the EEG data. Noise removal using wavelet has the characteristic of preservingsignal uniqueness even if noise is going to be minimized. To remove noise from EEG signal, this research employeddiscrete wavelet transform. Root mean square difference has been used to find the usefulness of the noiseelimination. In this research, four different discrete wavelet functions have been used to remove noise from theElectroencephalogram signal gotten from two different types of patients (healthy and epileptic) to show theeffectiveness of DWT on EEG noise removal. The result shows that the WF orthogonal meyer is the best one fornoise elimination from the EEG signal of epileptic subjects and the WF Daubechies 8 (db8) is the best one for noiseelimination from the EEG signal on healthy subjects.
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Al-Kadi et al. (2013) studied this question.
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