Independent component analysis (ICA) is a new technique suitable for separating independent components from electrocardiogram (ECG) complex signals. The basic idea of using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces and to decompose each rotation into elementary rotations within all two-dimensional planes spanned by the coordinate axes useful for diagnostic information of heart. In this paper, ability of ICA for parameterization of ECG signals was felt to reduce the amount of redundant ECG data. This work aims at finding an independent subspace analysis (ISA) model for ECG analysis that allows applicability to any random vectors available in an ECG data set. For the common standards for electrocardiography (CSE) based ECG data sets, joint approximate diagonalization of eigen matrices (Jade) algorithm is used to find smaller subspaces. The extracted independent components are further cleaned by statistical measures. In this study, it is also observed that the value of kurtosis coefficients for the independent components, which represents the noise component, can be further reduced using parameterized multidimensional ICA (PMICA) technique. The indeterminacies if available in the ECG data are to be analysed also using modified version of Jade algorithm to PMICA and parameterized standard ICA (PsICA) for comparative studies. The indeterminacies if available in the ECG data are reduced in PMICA better in comparison to the analysis done using PsICA. The simulation results obtained indicate that ICA definitely improves signal–noise ratio (SNR) like the other higher order digital filtering methods like Kalman, Butterworth etc. with minimum reconstruction errors. Here, it is also confirmed that re-parameterization of the standard ICA model results into a ‘component model’ using MICA technique, which is geometric in spirit and free of indeterminacies existing in sICA model.

中文翻译：

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使用参数化的多维独立分量分析检测校正的ECG信号中的不确定性

独立分量分析（ICA）是一种适用于从心电图（ECG）复杂信号中分离独立分量的新技术。使用多维独立分量分析（MICA）的基本思想是找到稳定的高维源信号子空间，并将每个旋转分解为所有二维平面内的基本旋转，这些二维平面由对心脏的诊断信息有用的坐标轴组成。在本文中，人们感觉到ICA可以对ECG信号进行参数设置，从而减少了冗余ECG数据量。这项工作旨在找到一种用于ECG分析的独立子空间分析（ISA）模型，该模型可应用于ECG数据集中可用的任何随机向量。对于基于心电图（CSE）的ECG数据集的通用标准，本征矩阵的联合近似对角化（Jade）算法用于查找较小的子空间。提取的独立成分将通过统计手段进一步清洗。在这项研究中，还观察到，使用参数化多维ICA（PMICA）技术可以进一步减小代表噪声分量的独立分量的峰度系数值。如果存在ECG数据中的不确定性，也应使用PMade的Jade算法的修改版本和参数化的标准ICA（PsICA）进行比较研究。与使用PsICA进行的分析相比，PMICA可以更好地减少ECG数据中的不确定性。获得的仿真结果表明，ICA与其他更高阶的数字滤波方法（例如Kalman，Butterworth等）一样，以最小的重构误差，确实提高了信噪比（SNR）。在此，还可以确定，使用MICA技术将标准ICA模型的重新参数化结果转化为“组件模型”，该模型本质上是几何形状的，并且没有sICA模型中存在的不确定性。