An online method for detecting nonlinearity within a signal

Beth Jelfs; Phebe Vayanos; Chen Mo; Lee Goh Su; Christos Boukis; Temujin Gautama; Tomasz Rutkowski; Tony Kuh; Danilo Mandic

doi:10.1007/11893011_154

An online method for detecting nonlinearity within a signal

Beth Jelfs^*, Phebe Vayanos, Chen Mo, Lee Goh Su, Christos Boukis, Temujin Gautama, Tomasz Rutkowski, Tony Kuh, Danilo Mandic

^*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

A novel method for online analysis of the changes in signal modality is proposed. This is achieved by tracking the dynamics of the mixing parameter within a hybrid filter rather than the actual filter performance. An implementation of the proposed hybrid filter using a combination of the Least Mean Square (LMS) and the Generalised Normalised Gradient Descent (GNGD) algorithms is analysed and the potential of such a scheme for tracking signal nonlinearity is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis. Biological applications of the approach have been illustrated on EEG data of epileptic patients.

Original language	English
Title of host publication	Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings
Publisher	Springer Verlag
Pages	1216-1223
Number of pages	8
ISBN (Print)	3540465421, 9783540465423
DOIs	https://doi.org/10.1007/11893011_154
Publication status	Published - 2006
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4253 LNAI - III
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/11893011_154

Cite this

Jelfs, B., Vayanos, P., Mo, C., Su, L. G., Boukis, C., Gautama, T., Rutkowski, T., Kuh, T., & Mandic, D. (2006). An online method for detecting nonlinearity within a signal. In Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings (pp. 1216-1223). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4253 LNAI - III). Springer Verlag. https://doi.org/10.1007/11893011_154

Jelfs, Beth ; Vayanos, Phebe ; Mo, Chen et al. / An online method for detecting nonlinearity within a signal. Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings. Springer Verlag, 2006. pp. 1216-1223 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An online method for detecting nonlinearity within a signal",

abstract = "A novel method for online analysis of the changes in signal modality is proposed. This is achieved by tracking the dynamics of the mixing parameter within a hybrid filter rather than the actual filter performance. An implementation of the proposed hybrid filter using a combination of the Least Mean Square (LMS) and the Generalised Normalised Gradient Descent (GNGD) algorithms is analysed and the potential of such a scheme for tracking signal nonlinearity is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis. Biological applications of the approach have been illustrated on EEG data of epileptic patients.",

author = "Beth Jelfs and Phebe Vayanos and Chen Mo and Su, {Lee Goh} and Christos Boukis and Temujin Gautama and Tomasz Rutkowski and Tony Kuh and Danilo Mandic",

year = "2006",

doi = "10.1007/11893011_154",

language = "English",

isbn = "3540465421",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1216--1223",

booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings",

}

Jelfs, B, Vayanos, P, Mo, C, Su, LG, Boukis, C, Gautama, T, Rutkowski, T, Kuh, T & Mandic, D 2006, An online method for detecting nonlinearity within a signal. in Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4253 LNAI - III, Springer Verlag, pp. 1216-1223. https://doi.org/10.1007/11893011_154

An online method for detecting nonlinearity within a signal. / Jelfs, Beth; Vayanos, Phebe; Mo, Chen et al.
Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings. Springer Verlag, 2006. p. 1216-1223 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4253 LNAI - III).

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

TY - GEN

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AU - Jelfs, Beth

AU - Vayanos, Phebe

AU - Mo, Chen

AU - Su, Lee Goh

AU - Boukis, Christos

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AU - Rutkowski, Tomasz

AU - Kuh, Tony

AU - Mandic, Danilo

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AB - A novel method for online analysis of the changes in signal modality is proposed. This is achieved by tracking the dynamics of the mixing parameter within a hybrid filter rather than the actual filter performance. An implementation of the proposed hybrid filter using a combination of the Least Mean Square (LMS) and the Generalised Normalised Gradient Descent (GNGD) algorithms is analysed and the potential of such a scheme for tracking signal nonlinearity is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis. Biological applications of the approach have been illustrated on EEG data of epileptic patients.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings

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Jelfs B, Vayanos P, Mo C, Su LG, Boukis C, Gautama T et al. An online method for detecting nonlinearity within a signal. In Knowledge-Based Intelligent Information and Engineering Systems - 10th International Conference, KES 2006, Proceedings. Springer Verlag. 2006. p. 1216-1223. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11893011_154

An online method for detecting nonlinearity within a signal

Abstract

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