Enhancement of the spatial resolution of ECG using multi-scale Linear Regression

Jiss J. Nallikuzhy, S. Dandapat
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引用次数: 7

Abstract

Spatial resolution of ECG can be increased using the information available from a subset of standard 12-lead ECG. This is usually achieved by learning a model between the standard 12-lead and its reduced lead subset. Since ECG signal contains significant amount of diagnostic information, it is important to learn a model which preserves this information. In this work, a patient specific model is proposed which utilizes the inter lead correlation in the transformed domain. The model is learned over Wavelet domain using Linear Regression. Performance of the model is evaluated using standard distortion measures such as correlation coefficient and root mean square error along with wavelet energy based diagnostic distortion. An analysis is also performed over the derived signal to quantify the loss of diagnostic information. The results show that the proposed model performs better in preserving diagnostic information in comparison to the existing linear models.
多尺度线性回归增强心电空间分辨率
利用标准12导联心电图子集提供的信息,可以提高心电图的空间分辨率。这通常是通过学习标准12导联及其减少导联子集之间的模型来实现的。由于心电信号包含大量的诊断信息,因此学习一种保留这些信息的模型是很重要的。在这项工作中,提出了一种针对患者的模型,该模型利用了转换域中的导联相关性。利用线性回归在小波域学习模型。利用相关系数和均方根误差等标准失真度量以及基于小波能量的诊断失真来评估模型的性能。还对派生信号进行了分析,以量化诊断信息的损失。结果表明,与现有的线性模型相比,该模型在保留诊断信息方面有更好的表现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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