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.
Recommended Citation
Povinelli, Richard J.; Johnson, Michael T.; Lindgren, Andrew C.; Roberts, Felice M.; and Ye, Jinjin, "Statistical Models of Reconstructed Phase Spaces for Signal Classification" (2006). Electrical and Computer Engineering Faculty Research and Publications. 54.
https://epublications.marquette.edu/electric_fac/54
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.