A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals

Z. S. Wang; Y. S. Xia; W. H. Li; Z. Y. He; J. D.Z. Chen

doi:10.1109/ICNN.1997.614218

A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals

Z. S. Wang, Y. S. Xia, W. H. Li, Z. Y. He, J. D.Z. Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper the authors present a new neural network model, called the constrained smallest l₁-norm neural network (CSl₁NN), for basis pursuit (BP) implementation. The BP is considered as a large-scale linear programming problem. In contrast with the simplex-BP or inferior-BP, the proposed CSl₁NN-BP does not double the optimizing scale and can be implemented in real time via hardware. Using non-stationary artificial signals and electrogastrograms to test our simulations show that the CSl ₁NN-BP presents an excellent convergence performance for a wide range of time-frequency (TF) dictionaries and has a higher joint TF resolution not only than the traditional Wigner distribution, but also other overcomplete representation methods. Combining the high resolution with the fast implementation, the CSl₁NN-BP can be used for online time-frequency analysis of various kinds of non-stationary signals including medical data, such as ECG, EEG and EGG.

Original language	English (US)
Title of host publication	1997 IEEE International Conference on Neural Networks, ICNN 1997
Pages	2057-2060
Number of pages	4
DOIs	https://doi.org/10.1109/ICNN.1997.614218
State	Published - 1997
Externally published	Yes
Event	1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States Duration: Jun 9 1997 → Jun 12 1997

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
Volume	4
ISSN (Print)	1098-7576

Conference

Conference	1997 IEEE International Conference on Neural Networks, ICNN 1997
Country/Territory	United States
City	Houston, TX
Period	6/9/97 → 6/12/97

ASJC Scopus subject areas

Software

Access to Document

10.1109/ICNN.1997.614218

Cite this

Wang, Z. S., Xia, Y. S., Li, W. H., He, Z. Y., & Chen, J. D. Z. (1997). A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals. In 1997 IEEE International Conference on Neural Networks, ICNN 1997 (pp. 2057-2060). Article 614218 (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 4). https://doi.org/10.1109/ICNN.1997.614218

A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals. / Wang, Z. S.; Xia, Y. S.; Li, W. H. et al.
1997 IEEE International Conference on Neural Networks, ICNN 1997. 1997. p. 2057-2060 614218 (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, ZS, Xia, YS, Li, WH, He, ZY & Chen, JDZ 1997, A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals. in 1997 IEEE International Conference on Neural Networks, ICNN 1997., 614218, IEEE International Conference on Neural Networks - Conference Proceedings, vol. 4, pp. 2057-2060, 1997 IEEE International Conference on Neural Networks, ICNN 1997, Houston, TX, United States, 6/9/97. https://doi.org/10.1109/ICNN.1997.614218

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abstract = "In this paper the authors present a new neural network model, called the constrained smallest l1-norm neural network (CSl1NN), for basis pursuit (BP) implementation. The BP is considered as a large-scale linear programming problem. In contrast with the simplex-BP or inferior-BP, the proposed CSl1NN-BP does not double the optimizing scale and can be implemented in real time via hardware. Using non-stationary artificial signals and electrogastrograms to test our simulations show that the CSl 1NN-BP presents an excellent convergence performance for a wide range of time-frequency (TF) dictionaries and has a higher joint TF resolution not only than the traditional Wigner distribution, but also other overcomplete representation methods. Combining the high resolution with the fast implementation, the CSl1NN-BP can be used for online time-frequency analysis of various kinds of non-stationary signals including medical data, such as ECG, EEG and EGG.",

author = "Wang, {Z. S.} and Xia, {Y. S.} and Li, {W. H.} and He, {Z. Y.} and Chen, {J. D.Z.}",

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AB - In this paper the authors present a new neural network model, called the constrained smallest l1-norm neural network (CSl1NN), for basis pursuit (BP) implementation. The BP is considered as a large-scale linear programming problem. In contrast with the simplex-BP or inferior-BP, the proposed CSl1NN-BP does not double the optimizing scale and can be implemented in real time via hardware. Using non-stationary artificial signals and electrogastrograms to test our simulations show that the CSl 1NN-BP presents an excellent convergence performance for a wide range of time-frequency (TF) dictionaries and has a higher joint TF resolution not only than the traditional Wigner distribution, but also other overcomplete representation methods. Combining the high resolution with the fast implementation, the CSl1NN-BP can be used for online time-frequency analysis of various kinds of non-stationary signals including medical data, such as ECG, EEG and EGG.

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A neural network solver for basis pursuit and its applications to time-frequency analysis of biomedical signals

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