CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

Abstract

Background: We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. Methods: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. Results: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ (‘tau’) where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded at https://bitbucket.org/deepak_panday/ceps/src/pipeline_v2/. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. Conclusions: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.

EntropyHub: An open-source toolkit for entropic time series analysis

“…The growing number of entropy methods reported in the scientific literature for time series and image analysis warrants new software tools that enable researchers to apply such methods [ 2 , 3 , 38 ]. Currently, there is a dearth of validated, open-source tools that implement a comprehensive array of entropy methods at the command-line with options to modify multiple parameter values.…”

“…Additionally, the utility of these packages is also limited for several reasons. The CEPS [ 38 ], EZ Entropy [ 37 ] and PyBioS [ 39 ] packages all operate through graphical user interfaces (GUIs) with facilities to plot and process data interactively. The interactive nature of GUIs can be beneficial when analysing small datasets but becomes burdensome when analysing large datasets where automated processing tasks are advantageous.…”

“…The interactive nature of GUIs can be beneficial when analysing small datasets but becomes burdensome when analysing large datasets where automated processing tasks are advantageous. Both the CEPS [ 38 ] and EZ Entropy [ 37 ] are designed for the MATLAB programming environment (MathWorks, MA, USA) which requires a purchased license in order to use. This paywall prevents many users from accessing the software and consequently impedes the replication of results achieved by using these packages.…”

“…Several packages offering entropy-related functions have been released in recent years [37][38][39], intended primarily for the analysis of physiological data, Table 1. Although these packages offer some useful tools, they lack the capacity to perform extensive data analysis with multiple methods from the cross-entropy [40], bidimensional entropy [41], and multiscale entropy [42] families of algorithms.…”

A New Fractional-Order Chaotic System with Its Analysis, Synchronization, and Circuit Realization for Secure Communication Applications

“…For example, Higuchi's method, the rescaled range method, Renyi's entropy, and Katz's method. Higuchi's fractal dimension (HFD) is a popular nonlinear measure for signal analysis and it is a time-domain signal complexity metric [66].…”