Assessing the convolutedness of multivariate physiological time series

Paolo Castiglioni; Giampiero Merati; Andrea Faini

doi:10.1109/EMBC.2014.6945002

Assessing the convolutedness of multivariate physiological time series

Paolo Castiglioni, Giampiero Merati, Andrea Faini

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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: FD_M =log(L)/log(d). In this work we also consider a correction of the Katz's estimator of FD_M, i.e., FD_KC =log(N)/(log(N)+log(d/L)), with N the number of samples; and FD_MC, an estimator of FD_M based on FD_KC calculated over a shorter running window N_w

Original language	English
Title of host publication	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6024-6027
Number of pages	4
ISBN (Print)	9781424479290
DOIs	https://doi.org/10.1109/EMBC.2014.6945002
Publication status	Published - Nov 2 2014
Event	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: Aug 26 2014 → Aug 30 2014

Other

Other	2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Country	United States
City	Chicago
Period	8/26/14 → 8/30/14

ASJC Scopus subject areas

Health Informatics
Computer Science Applications
Biomedical Engineering

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Assessing the convolutedness of multivariate physiological time series. / Castiglioni, Paolo ; Merati, Giampiero ; Faini, Andrea.

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 6024-6027 6945002.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Castiglioni, P , Merati, G & Faini, A 2014, Assessing the convolutedness of multivariate physiological time series. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014., 6945002, Institute of Electrical and Electronics Engineers Inc., pp. 6024-6027, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 8/26/14. https://doi.org/10.1109/EMBC.2014.6945002

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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

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