A feature of time-series variability that may reveal underlying complex dynamics is the degree of "convolutedness". For multivariate series of m components, convolutedness can be defined as the propensity of the trail of the time-series samples to fill the m-dimensional space. This work proposes different convolutedness indices and compare them on synthesized and real physiological signals. The indices are based on length L and planar extension d of the trail in m dimensions. The classical ones are: the L/d ratio, and the Mandelbrot's fractal dimension (FD) of a curve: FDM =log(L)/log(d). In this work we also consider a correction of the Katz’s estimator of FDM, i.e., FDKC =log(N)/(log(N)+log(d/L)), with N the number of samples; and FDMC, an estimator of FDM based on FDKC calculated over a shorter running window Nw.

Assessing the convolutedness of multivariate physiological time series / P. Castiglioni, G. Merati, A. Faini. ((Intervento presentato al 36. convegno Annual International IEEE EMBS Conference tenutosi a Chicago nel 2014.

Assessing the convolutedness of multivariate physiological time series

G. Merati
Secondo
;
2014

Abstract

A feature of time-series variability that may reveal underlying complex dynamics is the degree of "convolutedness". For multivariate series of m components, convolutedness can be defined as the propensity of the trail of the time-series samples to fill the m-dimensional space. This work proposes different convolutedness indices and compare them on synthesized and real physiological signals. The indices are based on length L and planar extension d of the trail in m dimensions. The classical ones are: the L/d ratio, and the Mandelbrot's fractal dimension (FD) of a curve: FDM =log(L)/log(d). In this work we also consider a correction of the Katz’s estimator of FDM, i.e., FDKC =log(N)/(log(N)+log(d/L)), with N the number of samples; and FDMC, an estimator of FDM based on FDKC calculated over a shorter running window Nw.
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Assessing the convolutedness of multivariate physiological time series / P. Castiglioni, G. Merati, A. Faini. ((Intervento presentato al 36. convegno Annual International IEEE EMBS Conference tenutosi a Chicago nel 2014.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/246059
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