Separation of Subcutaneous Fat From Muscle in Surface Electrical Impedance Myography Measurements Using Model Component Analysis
Separation of Subcutaneous Fat From Muscle in Surface Electrical Impedance Myography Measurements Using Model Component Analysis
复制标题
DOI：
10.1109/tbme.2018.2839977
复制
发表时间：
2019-02-01
影响因子：
4.6
通讯作者：
Sanchez, Benjamin
中科院分区：
文献类型：
--
作者：
Kwon, Hyeuknam;Malik, Wasim Q.;Sanchez, Benjamin
目的：电阻抗肌电描记术（EIM）是一种评价神经肌肉疾病（NMD）的新技术。尽管近年来神经病学界已采用使用表面电极的EIM（sEIM）的应用来评估NMD状态，但sEIM作为骨骼肌状况的生物标志物的灵敏度受到皮下脂肪（SF）组织的影响。在这里，我们开发了一种方法，能够从sEIM数据中删除SF的贡献。方法：我们评估独立成分分析（伊卡）和主成分分析（PCA）的目的。然后，我们介绍了所谓的模型成分分析（MCA）。所有的方法进行了验证与数值模拟，使用从SF和肌肉组织的阻抗数据。然后用在患病个体（n = 3）中进行的测量来测试所述方法。结果如下：仿真结果表明，MCA是分离SF和肌肉组织的阻抗的最准确的方法，准确率为99.2%，其次是伊卡的51.4%，最后是PCA的38.5%。来自在患者的肱三头肌上测量的sEIM数据的实验结果与使用超声成像获得的肌肉灰度水平值一致。结论：MCA可用于从sEIM数据中分离SF和肌肉组织的阻抗，从而提高检测肌肉变化的灵敏度。意义：MCA可通过消除因任何原因皮下脂肪组织过多的NMD患者中SF组织的混杂效应，使sEIM技术成为疾病进展和治疗反应的更好诊断工具和生物标志物。
Objective: Electrical impedance myography (EIM) is a relatively new technique to assess neuromuscular disorders (NMD). Although the application of EIM using surface electrodes (sEIM) has been adopted by the neurology community in recent years to evaluate NMD status, sEIM's sensitivity as a biomarker of skeletal muscle condition is impacted by subcutaneous fat (SF) tissue. Here, we develop a method that is able to remove the contribution of SF from sEIM data. Methods: We evaluate independent component analysis (ICA) and principal component analysis (PCA) for this purpose. Then, we introduce the so-called model component analysis (MCA). All methods are validated with numerical simulations using impedivity data from SF and muscle tissues. The methods are then tested with measurements performed in diseased individuals (n = 3). Results: Simulations demonstrate that MCA is the most accurate method at separating the impedivity of SF and muscle tissues with the accuracy being 99.2%, followed by ICA with 51.4%, and finally PCA with 38.5%. Experimental results from sEIM data measured on the triceps brachii of patients are consistent with muscle grayscale level values obtained using ultrasound imaging. Conclusion: MCA can be used to separate the impedivity of SF and muscle tissues from sEIM data, thus increasing the sensitivity to detect changes in the muscle. Significance: MCA can make the sEIM technique a better diagnostic tool and biomarker of disease progression and response to therapy by removing the confounding effect of SF tissue in NMD patients with excess subcutaneous fat tissue for any reason.