Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation | |
Dong, Xinzheng3,4; Chen, Chang5; Geng, Qingshan1; Zhang, Wensheng2; Zhang, Xiaohua Douglas5 | |
刊名 | IEEE ACCESS |
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 |
DOI | 10.1109/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 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000615026000001 |
资助机构 | Science and Technology Development Fund, Macau ; University of Macau |
内容类型 | 期刊论文 |
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.Guangdong Acad Med Sci, Guangdong Gen Hosp, Guangzhou 510080, Peoples R China 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100864, Peoples R China 3.South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China 4.Jilin Univ, Zhuhai Lab Key Lab Symbol Computat & Knowledge En, Minist Educ, Zhuhai Coll, Zhuhai 519041, Peoples R China 5.Univ Macau, CRDA, Fac Hlth Sci, Taipa, Macao, 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. |
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