Motion Artifact Removal Using Empirical Mode Decomposition Using Ks test Blind Source Separation and Wavelet Transform
DOI:
https://doi.org/10.18486/ijcsnt/14.1.004Keywords:
EEG, EEMD-ICA, ICA, DDWT, EEMD-DDWICAAbstract
For medical science image processing, the elimination of artifacts from physiological signals is an essential step. The acuteness in performance of healthcare technology has upgraded from the current hospital centric environment towards a portable ubiquitous approaches. The enormous cost of fine equipment and ensemble technologies has created a window for mathematical models that approximates the signal dynamics towards a superlative performance. The uncertainty in subsequent performance of these approaches introduced a dedicated research and past few decades have witnessed considerable improvement. In this paper an enhanced empirical approach to model the artifacts of a free signal is described followed by filtering mechanism using ICA and DDWT. The input EEG is a single channel and is converted into multichannel for ICA operations. The multi-channel EEG constructed using EEMD is filtered with fast ICA algorithm and DDWT is employed to reject any traces of artifacts left in signal. This system is tested on various platforms of evaluation and results pronounce the eligibility of proposed algorithm to stand on top of currently deployed algorithms on account of significant improvement in results.
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