Name
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<;N appropriately selected to reduce estimation bias. Synthesized fractional Brownian motions indicated that all the indices increase with FD, but differ for other aspects, namely the dependence on N; the capacity to estimate FD; or to distinguish between true bivariate and degenerate bivariate time series. Application on real multivariate recordings of muscular activity before and after exercise-induced fatigue suggests that these indices can be profitably used to identify complex changes in the dynamics of physiological signals. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6945002 | EMBC |
Keywords | DocType | Volume |
mandelbrots fractal dimension,convolutedness indices,exercise-induced fatigue,convolution,medical signal processing,multivariate recordings,muscular activity,fractional brownian motions,true bivariate time series,multivariate physiological time series,electromyography,physiological signals,katz estimator,time series,degenerate bivariate time series | Conference | 2014 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Castiglioni | 1 | 53 | 17.20 |
| Giampiero Merati | 2 | 24 | 5.25 |
| Andrea Faini | 3 | 31 | 10.45 |