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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Finite Rank Series Modeling for Discrimination of Non-stationary Signals
verfasst von : Lina Maria Sepulveda-Cano, Carlos Daniel Acosta-Medina, Germán Castellanos-Dominguez
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer Berlin Heidelberg
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The analysis of time-variant biosignals for classification tasks, usually requires a modeling that may handel their different dynamics and non–stationary components. Although determination of proper stationary data length and the model parameters remains as an open issue. In this work, time–variant signal decomposition through Finite Rank Series Modeling is carried out, aiming to find the model parameters. Three schemes are tested for OSA detection based on HRV recordings: SSA and DLM as linear decompositions and EDS as non–linear decomposition. Results show that EDS decomposition presents the best performance, followed by SSA. As a conclusion, it can be inferred that adding complexity at the linear model the trend is approximate to a simple non–linear model.