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Classification of biological signals using linear and nonlinear features

Balli, T., Palaniappan, Ramaswamy (2010) Classification of biological signals using linear and nonlinear features. Physiological Measurement, 31 (7). pp. 903-920. ISSN 0967-3334. (doi:10.1088/0967-3334/31/7/003) (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/10.1088/0967-3334/31/7/003

Abstract

This paper investigates the characterization ability of linear and nonlinear features and proposes combining such features in order to improve the classification of biological signals, in particular single-trial electroencephalogram (EEG) and electrocardiogram (ECG) data. For this purpose, three data sets composed of ECG, epileptic EEG and finger-movement EEG were utilized. The characterization ability of seven nonlinear features namely the approximate entropy, largest Lyapunov exponents, correlation dimension, nonlinear prediction error, Hurst exponent, higher order autocovariance and asymmetry due to time reversal are compared with two linear features namely the autoregressive (AR) reflection coefficients and AR model coefficients. The features were tested by their ability to differentiate between different classes of data using a linear discriminant analysis (LDA) method with tenfold cross-validation. The class separability of combined linear and nonlinear features was assessed using sequential floating forward search with linear discriminant analysis method (SFFS-LDA). The results demonstrated that linear and nonlinear features on their own provided comparable results for the ECG data set and the finger-movement EEG data set whilst the linear features provided a better class separability compared to nonlinear features for the epileptic EEG data set. Combining linear and nonlinear features demonstrated a significant improvement in the class separability for all of the data sets where an average improvement of 20.56% was obtained with the ECG data set, 7.45% with finger-movement data set and 6.62% with the epileptic EEG data set. Overall results suggest that the use of combined linear and nonlinear feature sets would be a better approach for the characterization and classification of biological signals such as EEG and ECG. © 2010 Institute of Physics and Engineering in Medicine.

Item Type: Article
DOI/Identification number: 10.1088/0967-3334/31/7/003
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - Physiol. Meas. [Field not mapped to EPrints] C2 - 20505216 [Field not mapped to EPrints] AD - School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Classification, ECG, EEG, Linear features, Nonlinear features, article, discriminant analysis, electrocardiography, electroencephalography, epilepsy, human, movement (physiology), nonlinear system, statistical model, time, Discriminant Analysis, Electrocardiography, Electroencephalography, Epilepsy, Humans, Linear Models, Movement, Nonlinear Dynamics, Time Factors, Discriminant Analysis, Electrocardiography, Electroencephalography, Epilepsy, Humans, Linear Models, Movement, Nonlinear Dynamics, Time Factors
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 12 Dec 2018 22:17 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70708 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
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