Ayllón David, Gil-Pita Roberto, Seoane Fernando
R&D Department, Fonetic, 28037 Madrid, Spain.
Signal Theory and Communications Department, University of Alcala, Alcalá de Henares, Spain.
PLoS One. 2016 Jun 30;11(6):e0156522. doi: 10.1371/journal.pone.0156522. eCollection 2016.
Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible.
生物电阻抗谱（BIS）测量误差可能由寄生杂散电容、阻抗失配、串扰或它们极有可能的组合引起。准确的检测和识别对于进一步分析极为重要，因为在某些情况下以及对于某些应用，某些测量伪像可以被校正、最小化甚至避免。在本文中，我们提出了一种稳健的方法来检测测量伪像的存在，并识别BIS测量中存在何种测量误差。该方法基于监督式机器学习，并使用一组新颖的通用特征来在不同导抗平面中进行测量表征。已使用一个复杂光谱BIS测量数据库进行了实验验证，该数据库来自不同的BIS应用，包含六种不同类型的误差以及无误差测量。该方法获得了较低的分类误差（0.33%），并显示出良好的泛化能力。由于特征和分类模式都相对简单，因此在当前生物电阻抗光谱仪硬件中实现此预处理任务是可行的。
