TY - JOUR
T1 - Robust time delay estimation of bioelectric signals using least absolute deviation neural network
AU - Wang, Zhishun
AU - He, Zhenya
AU - Chen, Jiande D.Z.
N1 - Funding Information:
Manuscript received October 30, 2003; revised August 5, 2004. This work was supported in part by the NIMH under Grant MH 16 434, Grant MH 59 139, and Grant MH 068 318. Asterisk indicates corresponding author. *Z. Wang is with the Department of Child Psychiatry and Brain Imaging, Columbia University and NYSPI, 1051 Riverside Drive, Unit 74, New PI 2509 New York, NY 10032 USA (e-mail: zw2105@columbia.edu). Z. He is with the Department of Radio Engineering, Southeast University, Nanjing, China. J. D. Z. Chen is with the Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX 77550 USA. Digital Object Identifier 10.1109/TBME.2004.843287
PY - 2005/3
Y1 - 2005/3
N2 - 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 L1 -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 1-norm model superior to Lp-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).
AB - 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 L1 -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 1-norm model superior to Lp-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).
KW - Gastric myoelectrical activity and myoelectrical migrating complex (MMC)
KW - L-norm optimization
KW - Least absolute deviation (LAD)
KW - Neural network
KW - Time delay estimation
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U2 - 10.1109/TBME.2004.843287
DO - 10.1109/TBME.2004.843287
M3 - Article
C2 - 15759575
AN - SCOPUS:14844293585
SN - 0018-9294
VL - 52
SP - 454
EP - 462
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
IS - 3
ER -