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This work describes a hybrid adaptive filtering technique, designed to optimize the removal of the electrocardiogram (ECG) from surface electromyography (sEMG) recordings. Using the adaptive noise cancellation technique (ANC), we minimize the error gradient based on combined Time-Frequency filtering enhancements. The validation on artificially prepared signals with a real data basis shows better frequency...
This study addresses the removal of electrocardiogram pulses (ECG) from surface electromyography signals (sEMG) in wireless sEMG electrodes. We describe a wavelet-compression inspired filtering technique in order to minimize the computational complexity required in wireless sEMG electrodes while providing higher filtering performance than when with standard means. Using semi-artificially prepared...
In this paper, we proposed a Dual-adapted Fast Block Least Mean Squares algorithm (DA-FBLMS) to remove electrocardiogram (ECG) and noise contaminations from surface electromyography signals (sEMG). Based on an adaptive noise cancelation (ANC) structure and artificial input signals, the ANC integrated proposed algorithm distinguishes itself by the use of an iterative method characterized by a varying...
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