A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm

摘要

The proposed paper, presents the construction of adaptive noise cancellation filter based on gray wolf optimization(GWO) optimization technique.The relative investigation of different strategies uncovers that the presentation of GWOcalculation is better in boisterous condition. The objective of proposed paper is structure ANC channel utilizing GWOmethod that improves association involving output with pure EMG signal.The results of proposed strategy are contrastedthrough gray wolf optimizer (GWO) and other evolutionary algorithms.The presentation of these calculations is assessedregarding signal-to-noise ratio (S_(SNR)), mean square error (S_(MSE)), maximum error (S_(ME)) mean, convergence rate (CR) pluscorrelation feature (S_r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECGnoise at different SNR levels. The ANC filter based on GWO technique provides 28 dB improvement in output SNR, 81%reduction in MSE, and 84% lower ME as compared to reported ANC filter based on RLS algorithm. Further, ANC filterbased on GWO technique provides 7 dB improvement in output SNR, 59.5% reduction in MSE, and 69.2% lower ME ascompared to recently reported ANC filter based on ABC-MR algorithm.