Title :
Statistical models of reconstructed phase spaces for signal classification
Author :
Povinelli, Richard J. ; Johnson, Michael T. ; Lindgren, Andrew C. ; Roberts, Felice M. ; Ye, Jinjin
Author_Institution :
Electr. & Comput. Eng. Dept., Marquette Univ., Milwaukee, WI, USA
fDate :
6/1/2006 12:00:00 AM
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.
Keywords :
Bayes methods; cardiology; maximum likelihood estimation; medical signal processing; multidimensional signal processing; neural nets; signal classification; signal reconstruction; speech recognition; time series; ANN; Bayes maximum likelihood; artificial neural networks; heart arrhythmia classification; multidimensional reconstructed phase space; nonparametric distributions; parametric distributions; signal classification; speech recognition; statistical models; time series signals; Artificial neural networks; Frequency domain analysis; Heart; Information analysis; Logistics; Multidimensional systems; Pattern classification; Probability; Signal analysis; Time series analysis; Reconstructed phase spaces (RPSs); signal classification; statistical models;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2006.873479