EZ Entropy: a software application for the entropy analysis of physiological time-series

Abstract

Background: Entropy analysis has been attracting increasing attentions in the recent two or three decades. It assesses complexity, or irregularity, of time-series which is extraordinarily relevant to physiology and diseases as demonstrated by tremendous studies. However, the complexity can hardly be appreciated by traditional methods including time-, frequency-domain analysis, and time-frequency analysis that are the common built-in options in commercialized measurement and statistical software. To facilitate the entropy analysis of physiological time-series, a new software application, namely EZ Entropy, was developed and introduced in this article.

Results: EZ Entropy was developed in MATLAB^® environment. It was programmed in an object-oriented style and was constructed with a graphical user interface. EZ Entropy is easy to operate through its compact graphical interface, thus allowing researchers without knowledge of programming like clinicians and physiologists to perform such kind of analysis. Besides, it offers various settings to meet different analysis needs including (1) processing single data recording, (2) batch processing multiple data files, (3) sliding window calculations, (4) recall, (5) displaying intermediate data and final results, (6) adjusting input parameters, and (7) exporting calculation results after the run or in real-time during the analysis. The analysis results could be exported, either manually or automatically, to comma-separated ASCII files, thus being compatible to and easily imported into the common statistical analysis software. Code-wise, EZ Entropy is object-oriented, thus being quite easy to maintain and extend.

Conclusions: EZ Entropy is a user-friendly software application to perform the entropy analysis of time-series, as well as to simplify and to speed up this useful analysis.

Keywords: Entropy; MATLAB; Program; Software.

Fig. 1

The graphical user interface of EZ Entropy. Regions: (1) menu; (2) file info; (3) setting; (4) status; (5) data display; (6) display of intermediate results; (7) results table. Buttons: (A) to execute; (B) to recall. Data and results are from an actual run

Fig. 2

Steps for single recording analysis. Steps ①–③ demonstrate users’ operations while step ④ shows the signal imported

Fig. 3

Data import wizard

Fig. 4

Shown the two tabs in region (3) of Fig. 1

Fig. 5

Display of intermediate results. Shown the distance map of embedding dimension m (upper left), $m+1$ (upper right), as well as the probability density function of embedding dimension m and $m+1$ in different colors (lower left), and the cumulative distribution function of dimension m and $m+1$ in different colors (lower right). For upper panels, both x- and y-axis are for the time point (or vector index). For lower panels, the x-axis is for the distance and y-axis for the probability

Fig. 6

An example of the output file. The file names are delibrately blurred here