IEEE Trans Biomed Eng. 2019 Feb;66(2):354-364. doi: 10.1109/TBME.2018.2839977. Epub 2018 May 23.
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.
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).
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.
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.
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.
电阻抗肌描记法（EIM）是一种评估神经肌肉疾病（NMD）的新技术。尽管近年来神经科领域已经采用表面电极（sEIM）应用 EIM 来评估 NMD 状态，但 sEIM 作为骨骼肌状况的生物标志物的敏感性受到皮下脂肪（SF）组织的影响。在这里，我们开发了一种能够从 sEIM 数据中去除 SF 贡献的方法。
为此，我们评估了独立成分分析（ICA）和主成分分析（PCA）。然后，我们引入了所谓的模型分量分析（MCA）。所有方法都使用来自 SF 和肌肉组织的阻抗数据进行数值模拟验证。然后，使用在患病个体中进行的测量（n=3）对这些方法进行了测试。
模拟表明，MCA 是最准确的方法，能够以 99.2%的准确度分离 SF 和肌肉组织的阻抗，其次是 ICA（51.4%）和 PCA（38.5%）。从患者肱三头肌测量的 sEIM 数据的实验结果与使用超声成像获得的肌肉灰度级值一致。
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敏感性。
MCA 可以使 sEIM 技术成为一种更好的诊断工具和疾病进展和对治疗反应的生物标志物，通过消除任何原因导致皮下脂肪组织过多的 NMD 患者中 SF 组织的混杂影响。
