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Exploring the differences in surface electromyographic signal between myofascial-pain and normal groups: Feature extraction through wavelet denoising and decomposition

机译:探索肌筋疼痛和正常组表面电拍摄信号的差异:通过小波去噪和分解的特征提取

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Upper-back myofascial pain is an increasingly significant syndrome associated with frequent computer using. However, the changes in neuromuscular functions incurred by myofascial pain are still under-discovered. This study aims to discover the changes in neuromuscular function on the taut band through signal analysis of surface electromyography. We first developed a fully automatic algorithm to detect the duration of an epoch of muscle contraction. Following that, the features of epochs in both time-domain and frequency-domain were extracted from the 13 patients to compare with the measurement from 13 normal subjects. The higher contraction strength with lower median frequency found in the patient group is similar to the reported changes with muscle fatigue. The signal was further analyzed by wavelet energy of 17 levels. The result shows that the energy measured from the patients exceeds that from the normal group at the low frequency band, suggesting that an increasing synchronization level of motor unit recruitment may cause the drop in the median frequency and the increase in contraction strength.
机译:上后肌筋疼痛是与频繁的计算机使用越来越重要的综合症。然而,肌肉疼痛疼痛所产生的神经肌肉功能的变化仍然被发现。本研究旨在通过表面探测信号分析发现通过地面肌电图的信号分析来发现拉紧带上的神经肌肉功能的变化。我们首先制定了一种全自动算法,以检测肌肉收缩时期的持续时间。在此之后,从13名患者中提取时间域和频域中时代和频域中的时期的特征,以便与13个正常受试者的测量相比。患者组中发现的较低中值频率的较高的收缩强度类似于报告的肌肉疲劳的变化。通过17水平的小波能进一步分析该信号。结果表明,从患者测量的能量超过低频带中的正常组,表明电机单元招生的同步水平的增加可能导致中值频率和收缩强度的增加。

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