Optimizing of recurrence plots for noise reduction

Abstract

We propose a way to automatically detect the best neighborhood size for a local projective noise reduction filter, where a typical problem is the proper identification of the noise level. Here we make use of concepts from the recurrence quantification analysis in order to adaptively tune the filter along the incoming time series. We define an index, to be computed via recurrence plots, whose minimum gives a clear indication of the best size of the neighborhood in the embedding space. Comparison of the local projective noise reduction filter using this optimization scheme with the state of the art is also provided.

Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit

“…A too large e includes also points into the neighborhood, which are simple consecutive points on the trajectory [46]. Several criteria for choosing the cutoff distance e proposed [47,48]. One approach uses a fixed number of neighbors, N n for every point of the trajectory, called fixed amount of nearest neighbors [49].…”

Detecting determinism using recurrence quantification analysis: Three test procedures

“…Based on this factMatassini et al (2002) design an algorithm to identify and filter the noise level present in the time series.…”

Intensity Analysis of Recurrence Plots for the Detection of Deterministic Signals in Noise

“…A different approach based on cross-recurrence analysis was also considered for the LPI problem where the incoming signal is known 4 , however this approach can be easily shown to be sub-optimal. RPs have also been used in the related problem of de-noising signals 5 . Our approach to recurrence-based detection focuses on the structure of a RP.…”