Skip to main content

A combined linear & nonlinear approach for classification of epileptic EEG signals

Balli, T., Palaniappan, Ramaswamy (2009) A combined linear & nonlinear approach for classification of epileptic EEG signals. In: Proceedings of the 4th International IEEE/EMBS Conference on Neural Engineering. . pp. 714-717. IEEE ISBN 978-1-4244-2073-5. (doi:10.1109/NER.2009.5109396) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/NER.2009.5109396

Abstract

The use of both linear autoregressive model coefficients and nonlinear measures for classification of EEG signals recorded from healthy subjects and epilepsy patients is investigated. A total of seven nonlinear measures namely the approximate entropy, largest lyapunov exponent, correlation dimension, nonlinear prediction error, hurst exponent, third order autocovariance, asymmetry due to time reversal, are used in this study. The class separability of individual and combined feature sets is measured using Linear Discriminant Analysis (LDA) algorithm where the multiple features are selected by sequential floating forward search (SFFS) algorithm. The results have shown that the use of combined feature sets provide a better characterization of EEG signals compared to individual features.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/NER.2009.5109396
Additional information: Unmapped bibliographic data: C7 - 5109396 [EPrints field already has value set] LA - English [Field not mapped to EPrints] J2 - Int. IEEE/EMBS Conf. Neural Eng., NER [Field not mapped to EPrints] AD - School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Conference Paper [Field not mapped to EPrints] A4 - National Institutes of Health, NIH; National Institute of Neurological Disorders and Stroke, NINDS; National Science Foundation, NSF [Field not mapped to EPrints] C3 - 2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 [Field not mapped to EPrints]
Uncontrolled keywords: Component, EEG, Linear autoregressive model, Nonlinear complexity measures, State space reconstruction, Component, EEG, Linear autoregressive model, Nonlinear complexity measures, State space reconstruction, Differential equations, Discriminant analysis, Lyapunov methods, Nonlinear control systems, Sequential switching, Electroencephalography
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 20:43 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70711 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
  • Depositors only (login required):