Jero S Edward, Bharathi K Divya, Ramakrishnan S
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:690-693. doi: 10.1109/EMBC44109.2020.9176599.
The nonstationarity measure of surface Electromyography (sEMG) signals provide an index for muscle fatigue conditions. In this paper, a new framework has been proposed for the analysis of sEMG signal using Instantaneous Spectral Centroid (ISC). The novelty of the proposed work is use of topological signal processing method to quantify the nonstationarity of sEMG signal. For this, the signals are recorded from the biceps brachii muscles of 25 healthy subjects in isometric contraction. The analytical signals corresponding to nonfatigue and fatigue segments are computed using Hilbert Transform. Further, topological features such as center of gravity (CoG), triangular area function (TAF) and ISC are calculated from the geometrical representation of a transformed signal. The result indicates the increase of TAF in fatigue condition and the significant right shift of CoG in x-axis for 80% of subjects. Importantly, the ISC estimate is decreased by 17% upon fatiguing for 84% of subjects. The obtained results show statistical significance with p < 0.05. It is observed that the shape parameters are varied in accordance with the changes observed in global characteristics of sEMG signals during muscle fatigue. The preliminary results show that the topological features are able to quantify the nonstationarity in sEMG signal. Therefore, the proposed method can be used as a fatigue index for diagnosing various neuromuscular disorders.Clinical Relevance-This method can be used to establish metrics of muscle fatigue for the benefit of physicians especially in the field of fitness, sports, pre and post-surgery surveillance and rehabilitation.
表面肌电图（sEMG）信号的非平稳性测量为肌肉疲劳状况提供了一个指标。本文提出了一种使用瞬时谱质心（ISC）分析sEMG信号的新框架。该工作的新颖之处在于使用拓扑信号处理方法来量化sEMG信号的非平稳性。为此，在等长收缩过程中从25名健康受试者的肱二头肌记录信号。使用希尔伯特变换计算对应于非疲劳和疲劳段的解析信号。此外，从变换后信号的几何表示中计算诸如重心（CoG）、三角形面积函数（TAF）和ISC等拓扑特征。结果表明，在疲劳状态下TAF增加，80%的受试者的CoG在x轴上显著右移。重要的是，84%的受试者在疲劳时ISC估计值下降了17%。所得结果具有统计学意义，p < 0.05。观察到形状参数根据肌肉疲劳期间sEMG信号全局特征的变化而变化。初步结果表明，拓扑特征能够量化sEMG信号中的非平稳性。因此，所提出的方法可作为诊断各种神经肌肉疾病的疲劳指标。临床相关性——该方法可用于建立肌肉疲劳指标，以造福医生，特别是在健身、运动、手术前后监测和康复领域。
