Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

Ronakben Bhavsar, Neil Davey, Na Helian, Yi Sun, Tony Steffert, David Mayor

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)
65 Downloads (Pure)


Recently, different algorithms have been suggested to improve Sample Entropy (SE) performance. Although new methods for calculating SE have been proposed, so far improving the efficiency (computational time) of SE calculation methods has not been considered. This research shows such an analysis of calculating a correlation between Electroencephalogram(EEG) and Heart Rate Variability(HRV) based on their SE values. Our results indicate that the parsimonious outcome of SE calculation can be achieved by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain.
Original languageEnglish
Pages (from-to)97-104
Number of pages8
JournalProcedia Computer Science
Publication statusPublished - 11 Dec 2018
EventThe 9th Annual International Conference on Biologically Inspired Cognitive Architectures - Prague, Czech Republic
Duration: 24 Aug 201828 Aug 2018
Conference number: The 9th


  • EEG
  • HRV
  • Pearson Correlation
  • Sample Entropy
  • Time Series Data Analysis


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