Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation
Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation
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DOI：
10.1016/j.cmpb.2016.02.008
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发表时间：
2016-05-01
影响因子：
6.1
通讯作者：
Escudero, Javier
中科院分区：
文献类型：
--
作者：
Azami, Hamed;Escudero, Javier
背景与目的：信号分割和棘波检测是生物医学信号处理的两个重要应用领域。通常，非平稳信号必须被分割成分段的平稳历元，或者在进一步分析之前，需要在噪声背景中找到尖峰。置换熵被用来评价时间序列的不规则性。PE在概念上简单，在结构上对伪像很健壮，在计算上也很快。它在许多应用中得到了广泛的应用，但它有两个关键的缺点。首先，当使用Bandt-Pompe过程对信号进行符号化时，仅考虑幅度值的顺序，并且丢弃关于幅度的信息。其次，在PE中，没有考虑每个嵌入向量中的相同幅值的影响。为了解决这些问题，我们提出了一种基于PE的新的熵度量：幅度感知置换熵(AAPE)。方法：AAPE在量化信号基元方面比经典的PE更灵活，因此它对信号的幅度变化和频率变化都很敏感。为了说明AAPE方法如何提高信号分割和棘波检测的质量，我们处理了一组人工合成的真实神经元信号、脑电信号和神经元数据。我们将AAPE在这些问题上的表现与最新的分割方法进行了比较，并使用重复方差分析和后自组织Tukey检验来评估差异的显著性。结果：在信号分割中，基于AAPE的方法的准确率高于传统的分割方法。在存在噪声的情况下，AAPE也会导致更稳健的结果。与PE不同，AAPE的尖峰检测结果表明，即使在单样本尖峰的情况下，AAPE也可以很好地检测尖峰。对于多样本尖峰，AAPE的变化大于PE。结论：我们引入了一种新的熵度量AAPE，它使我们能够在PE的公式中考虑幅度信息。AAPE算法可用于各种信号和图像处理领域中几乎所有基于不规则性的应用。我们还免费提供了AAPE的MatLab代码。(C)2016爱思唯尔爱尔兰有限公司。保留所有权利。
Background and objective: Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values is considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE).Methods: AAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey's test.Results: In signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE.Conclusion: We introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the formulation of PE. The AAPE algorithm can be used in almost every irregularity-based application in various signal and image processing fields. We also made freely available the Matlab code of the AAPE. (C) 2016 Elsevier Ireland Ltd. All rights reserved.