Abstract
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.
Highlights
Since entropy has been applied to the field of informatics [1], this measure of time series complexity has been continuously developed and improved
Based on the principle of parallel computing, we proposed a new method to quickly calculate sample entropy based on the OpenCL framework
Compared with the basic sample entropy algorithm and the other two algorithms reported in the previous literature, we found that our improved algorithm has the greatest acceleration effect for clinical data, for commonly used parameter values m = 2 and r = 0.15 ∗ SD and for different embedding dimensions m and different tolerances r
Summary
Since entropy has been applied to the field of informatics [1], this measure of time series complexity has been continuously developed and improved. It is widely used in various fields such as astronomy [2], economics [3], and biology [4]. Over the past 20 years, the two most commonly used types of entropy, approximate entropy [5] and sample entropy [6], have been used for the measurement of nonlinear complexity in biological signals. Approximate entropy to measure the complexity and unpredictability of systems.
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