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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:探索肌筋膜疼痛组和正常组之间表面肌电信号的差异:通过小波去噪和分解进行特征提取

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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:上背部肌筋膜疼痛是与频繁使用计算机相关的越来越严重的综合症。然而,肌筋膜疼痛引起的神经肌肉功能的变化仍未被发现。本研究旨在通过表面肌电图信号分析发现绷紧带神经肌肉功能的变化。我们首先开发了一种全自动算法来检测肌肉收缩的持续时间。随后,从13例患者中提取了时域和频域的特征,以与13例正常受试者的测量结果进行比较。在患者组中发现较高的收缩强度和较低的中位频率与所报告的肌肉疲劳变化相似。通过17级小波能量进一步分析了该信号。结果表明,从患者处测得的能量在低频段上超过了正常组的能量,表明运动单元募集的同步水平提高可能导致中位频率下降和收缩强度增加。

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