Robust time delay estimation of bioelectric signals using least absolute deviation neural network
Robust time delay estimation of bioelectric signals using least absolute deviation neural network
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DOI：
10.1109/tbme.2004.843287
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发表时间：
2005-02
影响因子：
4.6
通讯作者：
Zhishun Wang;Zhenya He;Jiande D. Z. Chen
中科院分区：
文献类型：
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作者：
Zhishun Wang;Zhenya He;Jiande D. Z. Chen
时延估计是现代信号处理中的一个重要问题，在生物医学信号的空间传播特征提取中也得到了广泛的应用。由于底层系统的极端复杂性和多变性，生物医学信号通常是非平稳的、不稳定的甚至是混沌的。此外，由于测量环境的限制，生物医学信号往往被噪声污染。因此，生物医学信号的时延估计是一个具有挑战性的问题。提出了一种新的基于最小绝对偏差神经网络(LADNN)的时延估计算法及其应用实验。LADNN是最小绝对偏差(LAD)优化模型的神经实现，也被称为无约束最小L/次1/范数模型，理论上证明了该模型的全局收敛。在所提出的基于LADNN的TDE算法中，使用移动平均(MA)模型对给定的信号进行建模。利用LADNN估计MA参数，时延对应于MA系数出现峰值的时间指数。由于L/次1/范数模型在非高斯噪声环境甚至混沌环境中优于L/次p/范数(p>；1)模型，特别是对于包含突变的信号(如具有尖峰序列或运动伪影的生物医学信号)或混沌动态过程，基于LADNN的时延估计算法比现有的基于小波域相关性和基于高阶谱的时延估计算法具有更好的鲁棒性。与这些传统方法，特别是当前最先进的基于HOS的时延估计方法不同，基于LADNN的方法不需要假设信号是非高斯的，噪声是高斯的，因此更适用于实际情况。在高斯、非高斯和混沌三种不同的噪声环境下进行了仿真实验，将所提出的TDE方法与现有的HOS方法进行了比较。通过实际应用实验，提取胃肌电活动(GMA)相邻两个通道之间的时延信息，以评估GMA在移行性肌电复合体(MMC)不同时相的空间传播特性。
The time delay estimation (TDE) is an important issue in modern signal processing and it has found extensive applications in the spatial propagation feature extraction of biomedical signals as well. Due to the extreme complexity and variability of the underlying systems, biomedical signals are usually nonstationary, unstable and even chaotic. Furthermore, due to the limitations of the measurement environments, biomedical signals are often noise-contaminated. Therefore, the TDE of biomedical signals is a challenging issue. A new TDE algorithm based on the least absolute deviation neural network (LADNN) and its application experiments are presented in this paper. The LADNN is the neural implementation of the least absolute deviation (LAD) optimization model, also called unconstrained minimum L/sub 1/-norm model, with a theoretically proven global convergence. In the proposed LADNN-based TDE algorithm, a given signal is modeled using the moving average (MA) model. The MA parameters are estimated by using the LADNN and the time delay corresponds to the time index at which the MA coefficients have a peak. Due to the excellent features of L/sub 1/-norm model superior to L/sub p/-norm (p>1) models in non-Gaussian noise environments or even in chaos, especially for signals that contain sharp transitions (such as biomedical signals with spiky series or motion artifacts) or chaotic dynamic processes, the LADNN-based TDE is more robust than the existing TDE algorithms based on wavelet-domain correlation and those based on higher-order spectra (HOS). Unlike these conventional methods, especially the current state-of-the-art HOS-based TDE, the LADNN-based method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian and, thus, it is more applicable in real situations. Simulation experiments under three different noise environments, Gaussian, non-Gaussian and chaotic, are conducted to compare the proposed TDE method with the existing HOS-based method. Real application experiment is conducted to extract time delay information between every two adjacent channels of gastric myoelectrical activity (GMA) to assess the spatial propagation characteristics of GMA during different phases of the migrating myoelectrical complex (MMC).