Fast Algorithm Based on Parallel Computing for Sample Entropy Calculation

IEEE Access

Chang Chen¹  Xiaohua Douglas Zhang¹  Qingshan Geng²  Wensheng Zhang³  Xinzheng Dong⁴ 

[1] CRDA, Faculty of Health Sciences, University of Macau, Taipa, Macau;Guangdong General Hospital, Guangdong Academy of Medical Science, Guangzhou, China;Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing, China;School of Software Engineering, South China University of Technology, Guangzhou, China;

关键词: Algorithm;    fast computation;    graphics processing unit;    parallel computing;    sample entropy;   

DOI  :  10.1109/ACCESS.2021.3054750

来源: DOAJ

【 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.

【 授权许可】

Unknown