NDLI: Robust time delay estimation of bioelectric signals using least absolute deviation neural network

Content Provider	IEEE Xplore Digital Library
Author	Zhishun Wang Zhenya He Chen, J.D.Z.
Copyright Year	1964
Abstract	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).
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	9
File Size	388786
Starting Page	454
Ending Page	462
File Format	PDF
ISSN	00189294
Volume Number	52
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Delay effects Delay estimation Bioelectric phenomena Neural networks Chaos Working environment noise Signal processing Signal processing algorithms Gaussian noise time delay estimation Gastric myoelectrical activity and myoelectrical migrating complex (MMC) least absolute deviation (LAD) neural network
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Neural implementation of unconstrained minimum L/sub 1/-norm optimization-least absolute deviation model and its application to time delay estimation

A Generalized Least Absolute Deviation Method for Parameter Estimation of Autoregressive Signals

Constrained Least Absolute Deviation Neural Networks

Robust time delay estimation using lower-order statistics

Cooperative Recurrent Neural Networks for the Constrained $L_{1}$ Estimator

Robust signal modeling through nonlinear least squares

Estimation of the Parameters of Sinusoidal Signals in Non-Gaussian Noise

Adaptive signal enhancement by a robust TLS estimation

Suitable is the best: Least absolute deviation algorithm under high-mobility non-Gaussian noise environments

Robust time delay estimation of bioelectric signals using least absolute deviation neural network

Similar Documents

Neural implementation of unconstrained minimum L/sub 1/-norm optimization-least absolute deviation model and its application to time delay estimation

A Generalized Least Absolute Deviation Method for Parameter Estimation of Autoregressive Signals

Constrained Least Absolute Deviation Neural Networks

Robust time delay estimation using lower-order statistics

Cooperative Recurrent Neural Networks for the Constrained $L_{1}$ Estimator

Robust signal modeling through nonlinear least squares

Estimation of the Parameters of Sinusoidal Signals in Non-Gaussian Noise

Adaptive signal enhancement by a robust TLS estimation

Suitable is the best: Least absolute deviation algorithm under high-mobility non-Gaussian noise environments

Robust time delay estimation of bioelectric signals using least absolute deviation neural network