Document Type

Article

Language

eng

Format of Original

9 p.

Publication Date

6-2006

Publisher

Institute of Electrical and Electronics Engineers

Source Publication

IEEE Transactions on Signal Processing

Source ISSN

1053-587X

Original Item ID

doi: 10.1109/TSP.2006.873479

Abstract

This paper introduces a novel approach to the analysis and classification of time series signals using statistical models of reconstructed phase spaces. With sufficient dimension, such reconstructed phase spaces are, with probability one, guaranteed to be topologically equivalent to the state dynamics of the generating system, and, therefore, may contain information that is absent in analysis and classification methods rooted in linear assumptions. Parametric and nonparametric distributions are introduced as statistical representations over the multidimensional reconstructed phase space, with classification accomplished through methods such as Bayes maximum likelihood and artificial neural networks (ANNs). The technique is demonstrated on heart arrhythmia classification and speech recognition. This new approach is shown to be a viable and effective alternative to traditional signal classification approaches, particularly for signals with strong nonlinear characteristics.

Comments

Accepted version. IEEE Transactions on Signal Processing, Vol. 54, No. 6 (June 2006): 2178-2186. DOI. © 2006 Institute of Electrical and Electronics Engineers (IEEE). Used with permission.

Share

COinS