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首页> 外文会议>International Symposium on Medical Measurements and Applications >Muscle activity detection in pathological, weak and noisy myoelectric signals
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Muscle activity detection in pathological, weak and noisy myoelectric signals

机译：在病理性，微弱和嘈杂的肌电信号中进行肌肉活动检测

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The estimation of the on–off timing of the human skeletal muscles during movement is a critical issue in weak and noisy myoelectric signal processing, in motor control studies and in clinical applications. In this study, we used an approach based on the continuous wavelet transform to detect the muscular activity, in muscle with low amplitude and low SNR. The method is based on the calculation of a so-called “manifestation variable” computed as the maximum output of a bank of matched filters at different scales. EMG signals were generated by simulation using software tool based on an EMG mathematical model and different thresholds were applied for estimating the muscle on– off timing in simulated pathological, weak and noisy (several low SNR values were analyzed) myoelectric signals. The true timing of the EMG activity and the estimated timing of the EMG activity were compared by using a relative percentage error criterion. We performed a two-way ANOVA test, with SNR and threshold as factors, to determine possible significant effects on the relative percentage error. Our results showed that this approach shows satisfactory performances especially when proper threshold values are chosen. In particular, despite the estimated timing of the EMG activity approaches the true timing when SNR is higher, the method works well also for very low SNR. Therefore, this approach to estimate the on–off timing of muscles could be used to study pathological, weak and noisy myoelectric signals.
机译：在运动控制研究和临床应用中，在运动过程中人体骨骼肌的开-关时间估计是弱微且嘈杂的肌电信号处理中的关键问题。在这项研究中，我们使用了基于连续小波变换的方法来检测低振幅和低SNR的肌肉中的肌肉活动。该方法基于所谓的“表现变量”的计算，该“表现变量”被计算为一组不同尺度的匹配滤波器的最大输出。使用基于EMG数学模型的软件工具通过模拟生成EMG信号，并应用不同的阈值来估计模拟病理，微弱和嘈杂（分析了多个低SNR值）肌电信号中的肌肉开合时间。通过使用相对百分比误差标准，比较了EMG活动的真实时机和EMG活动的估计时机。我们以信噪比（SNR）和阈值为阈值进行了双向ANOVA测试，以确定对相对百分比误差的可能重大影响。我们的结果表明，这种方法表现出令人满意的性能，尤其是在选择适当的阈值时。尤其是，尽管当SNR较高时，EMG活动的估算时间接近真实时间，但该方法也适用于非常低的SNR。因此，这种估计肌肉通断时间的方法可用于研究病理性，微弱和嘈杂的肌电信号。

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