Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, UK.
IEEE Trans Biomed Eng. 2012 Feb;59(2):428-34. doi: 10.1109/TBME.2011.2173936. Epub 2011 Oct 28.
Original adaptive line enhancer (ALE) is used for denoising periodic signals from white noise. ALE, however, relies mainly on second order similarity between the signal and its delayed version and is more effective when the signal is narrowband. A new ALE based on singular spectrum analysis (SSA) is proposed here. In this approach in the reconstruction stage of SSA, the eigentriples are adaptively selected (filtered) using the delayed version of the data. Unlike the conventional ALE where (second) order statistics are taken into account, here the full eigen-spectrum of the embedding matrix is exploited. Consequently, the system works for non-Gaussian noise and wideband periodic signals. By performing some experiments on synthetic signals it is demonstrated that the proposed system is very effective for separation of biomedical data, which often have some periodic or quasi-periodic components, such as EMG affected by ECG artefacts. This data are examined here.
原始自适应线增强器（ALE）用于从白噪声中去除周期性信号。ALE 主要依赖于信号与其延迟版本之间的二阶相似性，并且在信号为窄带时更有效。本文提出了一种基于奇异谱分析（SSA）的新 ALE。在这种方法中，在 SSA 的重建阶段，使用数据的延迟版本自适应地选择（过滤）本征元。与考虑二阶统计量的传统 ALE 不同，这里利用了嵌入矩阵的完整本征谱。因此，该系统适用于非高斯噪声和宽带周期性信号。通过对合成信号进行一些实验，证明了所提出的系统对于分离生物医学数据非常有效，这些数据通常具有一些周期性或准周期性成分，例如受到 ECG 干扰的 EMG。这里检查了这些数据。
