Electrical impedance myography (EIM) technology is finding application in neuromuscular disease research as a tool to assess muscle health.
Correlations between electrical impedance myography outcomes, functional, imaging and histological data have been established in a variety of neuromuscular disorders.
Electrical impedance myography (EIM) is a non-invasive technique for the evaluation of neuromuscular disease that relies upon the application and measurement of high-frequency, low-intensity electrical current.
The EIM technique has proven useful in the diagnosis of radiculopathy, with specificity and sensitivity similar to that of needle electromyography (EMG).
Unlike needle EMG, however, there is no dichotomous outcome (e.g., presence vs absence of fibrillation potentials). For this reason and also because there is a fairly wide range of normal values, in order to employ EIM for this purpose, it is necessary to compare measures on the affected side to those on the unaffected side.
Differing results notwithstanding, the concept of using impedance to measure contractile properties in health and disease is attractive as it could offer new insights into the mechanics of muscle contraction, one area in which standard electrophysiologic techniques are relatively weak. In fact, current standard approaches such as needle EMG or nerve conduction studies only measure up to the point of muscle fiber depolarization, ignoring entirely the contractile process itself.
The tongue is an extraordinarily complex muscle, with fibres running through multiple planes. Thus, assessment of muscle impedance in a number of different directions using multiple frequencies should encapsulate maximal information.
Yet, a potential problem lies in interpreting this large amount of data to give an objective measure of disease. Thus, decomposition of the data is required in order to draw out the most important features and facilitate interpretation/graphical representation.
The authors of this article used a new dimension reduction technique (Non-negative tensor factorisation), to provide a framework for identifying clinically relevant features within a high dimensional EIM dataset.
Non-negative tensor factorisation was applied to the dataset for dimensionality reduction. It provides highly significant differentiation between healthy and Amyotrophic Lateral Sclerosis patients.
Similarly this new technique to analyze datasets differentiates between mild and severe disease states and significantly correlates with symptoms.
Tensor EIM thus can provide clinically relevant metrics for identifying Amyotrophic Lateral Sclerosis- related muscle disease.
This procedure has the advantage of using the whole spectral dataset, with reduced risk of overfitting. The process identifies spectral shapes specific to disease allowing for a deeper clinical interpretation.