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.
|Number of pages||9|
|Journal||IEEE Transactions on Signal Processing|
|Issue number||6 I|
|State||Published - Jun 2006|
Bibliographical noteFunding Information:
Manuscript received July 22, 2004; revised July 11, 2005. This work was supported in part by the National Science Foundation by Grant IIS-0113508, by the Department of Education GAANN Fellowship, by an American Heart Association Fellowship, and by the Milwaukee Foundation Frank Rogers Bacon Research Assistantship. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Tulay Adali.
- Reconstructed phase spaces (RPSs)
- Signal classification
- Statistical models
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering