Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation
Dong X.1; Chen C.2; Geng Q.3; Zhang W.4; Zhang X.D.2
Source PublicationIEEE Access

Sample entropy is a widely used method for assessing the irregularity of physiological signals, but it has a high computational complexity, which prevents its application for time-sensitive scenes. To improve the computational performance of sample entropy analysis for the continuous monitoring of clinical data, a fast algorithm based on OpenCL was proposed in this paper. OpenCL is an open standard supported by a majority of graphics processing unit (GPU) and operating systems. Based on this protocol, a fast-parallel algorithm, OpenCLSampEn, was proposed for sample entropy calculation. A series of 24-hour heartbeat data were used to verify the robustness of the algorithm. Experimental results showed that OpenCLSampEn exhibits great accelerating performance. With common parameters, this algorithm can reduce the execution time to 1/75 of the base algorithm when the signal length is larger than 60,000. OpenCLSampEn also exhibits robustness for different embedding dimensions, tolerance thresholds, scales and operating systems. In addition, an R package of the algorithm is provided in GitHub. We proposed a sample entropy fast algorithm based on OpenCL that exhibits significant improvement for the computation performance of sample entropy. The algorithm has broad utility in sample entropy when facing the challenge of future rapid growth in the quantity of continuous clinical and physiological signals.

KeywordAcceleration Algorithm Biomedical Monitoring Entropy Fast Computation Graphics Processing Unit Graphics Processing Units Kernel Parallel Computing Sample Entropy Standards Time Series Analysis
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Indexed BySCIE
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000615026000001
The Source to ArticleScopus
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang X.D.
Affiliation1.School of Software Engineering, South China University of Technology Guangzhou, China and Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University Zhuhai, China.;
2.CRDA, Faculty of Health Sciences, University of Macau, Taipa, Macau.;
3.Guangdong General Hospital, Guangdong Academy of Medical Science, Guangzhou, China.;
4.Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Corresponding Author AffilicationFaculty of Health Sciences
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GB/T 7714
Dong X.,Chen C.,Geng Q.,et al. Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation[J]. IEEE Access,2021.
APA Dong X.,Chen C.,Geng Q.,Zhang W.,&Zhang X.D..(2021).Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation.IEEE Access.
MLA Dong X.,et al."Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation".IEEE Access (2021).
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