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

Peng Li
2019 BioMedical Engineering OnLine  
It is a contemporary challenge to identify characteristics from physiological signals or time-series that are relevant to aging or disease progression. Efforts have been made in using moments, either lower-ordered or higher-ordered, frequency-domain analysis and time-frequency analysis to mine the data. But results based upon these traditional approaches so far are not quite satisfactory. One of the possible reasons might be that those that could be captured by these traditional analyses are
more » ... nal analyses are usually also visually identifiable which is not the case for physiological data. What is relevant to physiology or 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.
doi:10.1186/s12938-019-0650-5 fatcat:bvl4nuhmcrezph4ugmrjj42mvy