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首页> 外文期刊>IEEE Transactions on Biomedical Engineering >Robust time delay estimation of bioelectric signals using least absolute deviation neural network
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Robust time delay estimation of bioelectric signals using least absolute deviation neural network

机译：使用最小绝对偏差神经网络的生物电信号鲁棒时延估计

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摘要

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).
机译：时延估计（TDE）是现代信号处理中的一个重要问题，并且已在生物医学信号的空间传播特征提取中找到了广泛的应用。由于基础系统的极端复杂性和可变性，生物医学信号通常是不稳定的，不稳定的甚至是混乱的。此外，由于测量环境的限制，生物医学信号经常被噪声污染。因此，生物医学信号的TDE是一个具有挑战性的问题。提出了一种基于最小绝对偏差神经网络的TDE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，该模型也称为无约束最小L / sub 1 /范数模型，具有经理论验证的全局收敛性。在提出的基于LADNN的TDE算法中，使用移动平均（MA）模型对给定信号进行建模。通过使用LADNN估计MA参数，并且时间延迟对应于MA系数达到峰值的时间索引。由于L / sub 1 /范数模型的出色功能在非高斯噪声环境或什至是混乱的情况下优于L / sub p /范数（p> 1）模型，特别是对于包含急剧过渡的信号（例如具有尖峰序列或运动伪像的生物医学信号）或混沌动态过程，基于LADNN的TDE比现有的基于小波域相关和基于高阶谱（HOS）的TDE算法更健壮。与这些常规方法不同，特别是与当前基于HOS的最新TDE不同，基于LADNN的方法没有信号是非高斯且噪声是高斯的假设，因此，它更适用在实际情况下。进行了三种不同噪声环境（高斯，非高斯和混沌）下的仿真实验，以将建议的TDE方法与现有的基于HOS的方法进行比较。进行了实际应用实验，以提取胃肌电活动（GMA）的每两个相邻通道之间的时间延迟信息，以评估GMA在迁移肌电复合体（MMC）不同阶段期间的空间传播特性。

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