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.

中文翻译：

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使用自适应增强松鼠搜索算法从 EMG 信号中去除噪声

肌电图 (EMG) 信号主要受到大量伪影的影响。最常见的影响伪影是电力线干扰 (PLW)、基线噪声和 ECG 噪声。这项工作的重点是一种新的衰减噪声去除策略，该策略集中在自适应滤波概念上。在本文中，采用增强型松鼠搜索 (ESS) 算法来使用自适应滤波器去除噪声。设计了自适应最小均方（LMS）滤波器和自适应递归最小二乘（RLS）滤波器，并与ESS相关联。这种新颖的算法比其他现有算法产生更好的性能。这里的性能是根据分贝的信噪比 (SNR)、最大误差 (ME)、均方误差 (MSE)、标准偏差、模拟时间和平均值差来衡量的。所提出的工作已在 MATLAB 仿真平台上实现。还使用不同的进化算法验证了它们的噪声衰减能力测试，即松鼠搜索、粒子群优化 (PSO)、人工蜂群 (ABC)、萤火虫、蚁群优化 (ACO) 和布谷鸟搜索 (CS)。所提出的工作消除了噪声，并在输出端提供了无噪声的 EMG 信号，与现有方法相比，这是高效的。我们提出的工作比结果中提到的文献实现了 4%、40%、4%、7%、9% 和 70% 的性能。人工蜂群 (ABC)、萤火虫、蚁群优化 (ACO) 和布谷鸟搜索 (CS)。所提出的工作消除了噪声，并在输出端提供了无噪声的 EMG 信号，与现有方法相比，这是高效的。我们提出的工作比结果中提到的文献实现了 4%、40%、4%、7%、9% 和 70% 的性能。人工蜂群 (ABC)、萤火虫、蚁群优化 (ACO) 和布谷鸟搜索 (CS)。所提出的工作消除了噪声，并在输出端提供了无噪声的 EMG 信号，与现有方法相比，这是高效的。我们提出的工作比结果中提到的文献实现了 4%、40%、4%、7%、9% 和 70% 的性能。