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Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation

文献类型：期刊论文

作者Dong, Xinzheng2,4; Chen, Chang1; Geng, Qingshan3; Zhang, Wensheng5; Zhang, Xiaohua Douglas1
刊名IEEE ACCESS
出版日期2021
卷号9页码:20223-20234
关键词Entropy Graphics processing units Kernel Time series analysis Biomedical monitoring Standards Acceleration Algorithm fast computation graphics processing unit parallel computing sample entropy
ISSN号2169-3536
DOI10.1109/ACCESS.2021.3054750
通讯作者Zhang, Xiaohua Douglas(douglaszhang@um.edu.mo)
英文摘要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.
资助项目Science and Technology Development Fund, Macau[0004/2019/AFJ] ; Science and Technology Development Fund, Macau[0011/2019/AKP] ; University of Macau[FHS-CRDA-029-002-2017] ; University of Macau[EF005/FHS-ZXH/2018/GSTIC] ; University of Macau[MYRG2018-00071-FHS]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000615026000001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Science and Technology Development Fund, Macau ; University of Macau
源URL[http://ir.ia.ac.cn/handle/173211/43097]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Zhang, Xiaohua Douglas
作者单位1.Univ Macau, CRDA, Fac Hlth Sci, Taipa, Macao, Peoples R China
2.South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China
3.Guangdong Acad Med Sci, Guangdong Gen Hosp, Guangzhou 510080, Peoples R China
4.Jilin Univ, Zhuhai Lab Key Lab Symbol Computat & Knowledge En, Minist Educ, Zhuhai Coll, Zhuhai 519041, Peoples R China
5.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100864, Peoples R China
推荐引用方式
GB/T 7714		 Dong, Xinzheng,Chen, Chang,Geng, Qingshan,et al. Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation[J]. IEEE ACCESS,2021,9:20223-20234.
APA Dong, Xinzheng,Chen, Chang,Geng, Qingshan,Zhang, Wensheng,&Zhang, Xiaohua Douglas.(2021).Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation.IEEE ACCESS,9,20223-20234.
MLA Dong, Xinzheng,et al."Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation".IEEE ACCESS 9(2021):20223-20234.

入库方式： OAI收割

来源：自动化研究所

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