Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

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

doi:10.1016/j.procs.2018.11.016

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 journal › Conference article › peer-review

3 Citations (Scopus)

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Abstract

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 language	English
Pages (from-to)	97-104
Number of pages	8
Journal	Procedia Computer Science
Volume	145
DOIs	https://doi.org/10.1016/j.procs.2018.11.016
Publication status	Published - 11 Dec 2018
Event	The 9th Annual International Conference on Biologically Inspired Cognitive Architectures - Prague, Czech Republic Duration: 24 Aug 2018 → 28 Aug 2018 Conference number: The 9th http://bica2018.bicasociety.org/

Keywords

EEG
HRV
Pearson Correlation
Sample Entropy
Time Series Data Analysis

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10.1016/j.procs.2018.11.016Licence: CC BY-NC-ND

1-s2.0-S1877050918323019-mainFinal published version, 762 KBLicence: CC BY-NC-ND

Cite this

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title = "Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis",

abstract = "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.",

keywords = "EEG, HRV, Pearson Correlation, Sample Entropy, Time Series Data Analysis",

author = "Ronakben Bhavsar and Neil Davey and Na Helian and Yi Sun and Tony Steffert and David Mayor",

year = "2018",

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doi = "10.1016/j.procs.2018.11.016",

language = "English",

volume = "145",

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journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier",

note = "The 9th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2018 ; Conference date: 24-08-2018 Through 28-08-2018",

url = "http://bica2018.bicasociety.org/",

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TY - JOUR

T1 - Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

AU - Bhavsar, Ronakben

AU - Davey, Neil

AU - Helian, Na

AU - Sun, Yi

AU - Steffert, Tony

AU - Mayor, David

N1 - Conference code: The 9th

PY - 2018/12/11

Y1 - 2018/12/11

N2 - 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.

AB - 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.

KW - EEG

KW - HRV

KW - Pearson Correlation

KW - Sample Entropy

KW - Time Series Data Analysis

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DO - 10.1016/j.procs.2018.11.016

M3 - Conference article

SN - 1877-0509

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SP - 97

EP - 104

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - The 9th Annual International Conference on Biologically Inspired Cognitive Architectures

Y2 - 24 August 2018 through 28 August 2018

ER -

Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

Abstract

Keywords

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