Key result
The enhanced squirrel search algorithm with adaptive filters achieved 4% to 70% better performance in noise removal from EMG signals compared to existing evolutionary algorithms.
Why the study?
Electromyogram signals are commonly affected by artifacts, including power line interference, baseline noise, and ECG noise.
An enhanced squirrel search algorithm with adaptive filtering effectively removes noise from EMG signals, outperforming several existing evolutionary algorithms in simulation.
Offers a promising computational approach for electromyography signal processing; leaves open whether this simulated noise reduction.
Electromyogram (EMG) signals are mostly affected by a large number of artifacts. Most commonly affecting artifacts are power line interference (PLW), baseline noise and ECG noise. This work focuses on a novel attenuation noise removal strategy which is concentrated on adaptive filtering concepts. In this paper, an enhanced squirrel search (ESS) algorithm is applied to remove noise using adaptive filters. The noise eliminating filters namely adaptive least mean square (LMS) filter and adaptive recursive least square (RLS) filters are designed, which is correlated with an ESS. This novel algorithm yields better performance than other existing algorithms. Here the performances are measured in terms of signal-to-noise ratio (SNR) in decibel, maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The proposed work has been implemented at the MATLAB simulation platform. Testing of their noise attenuation capability is also validated with different evolutionary algorithms namely squirrel search, particle swarm optimization (PSO), artificial bee colony (ABC), firefly, ant colony optimization (ACO) and cuckoo search (CS). The proposed work eliminates the noises and provides noise-free EMG signal at the output which is highly efficient when compared with existing methodologies. Our proposed work achieves 4%, 40%, 4%, 7%, 9% and 70% better performance than the literature mentioned in the results.
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Nagasirisha et al. (2020) studied Electromyogram (EMG) signal noise. Enhanced squirrel search (ESS) algorithm with adaptive filters vs. Squirrel search, PSO, ABC, firefly, ACO, and CS algorithms was evaluated on Signal-to-noise ratio (SNR), maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The enhanced squirrel search algorithm with adaptive filters achieved 4% to 70% better performance in noise removal from EMG signals compared to existing evolutionary algorithms.
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