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首页> 外文期刊>Journal of Neuroscience Methods >Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials.
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Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials.

机译：基于ICA分解和小波变换的降噪技术，用于提取电机动作电位。

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We have studied methods for noise reduction of myoelectric signals and for extraction of motor unit action potentials from these signals. Effective MUAP peak detection is the first important step in EMG decomposition. We first combined independent component analysis and wavelet filtering to remove power line interference, and then applied a wavelet filtering method and threshold estimation calculated using wavelet transform to suppress background noise and Gaussian white noise. The technique was applied to single-channel, short-period real myoelectric signals from normal subjects and to artificially generated EMG recordings. In contrast to existing methods based on amplitude single-threshold filtering of the original myoelectric signal or a conventional digitally filtered signal, our technique is fast and robust. Moreover, the proposed algorithm is substantially automatic. The performance has been evaluated with a set of synthetic and experimentally recorded myoelectric signals. The basic tool for testing was power spectrum density (PSD) estimation by the Welch method, which allowed us to analyze the PSD of nonstationary signals.
机译：我们已经研究了减少肌电信号降噪的方法，以及从这些信号中提取电机动作电位的方法。有效的MUAP峰检测是EMG分解的第一步。我们首先将独立分量分析与小波滤波相结合以消除电力线干扰，然后应用小波滤波方法和使用小波变换计算的阈值估计来抑制背景噪声和高斯白噪声。该技术被应用于来自正常受试者的单通道，短周期的真实肌电信号，以及人工生成的EMG记录。与基于原始肌电信号或常规数字滤波信号的幅度单阈值滤波的现有方法相比，我们的技术快速且可靠。而且，所提出的算法基本上是自动的。使用一组合成的和实验记录的肌电信号对性能进行了评估。测试的基本工具是通过Welch方法估算功率谱密度（PSD），这使我们能够分析非平稳信号的PSD。

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