Publication Details
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Chao Chen, Chengyu Liu, Jianqing Li, Bruno da Silva Gomes
 

IEEE Transactions on Instrumentation and Measurement

Contribution To Journal

Abstract 

Sample Entropy (SampEn) is widely used to assess the complexity of physiological time-series signals. However, it is a computationally intensive algorithm with O(N2) time complexity. Although algorithmic optimizations, such as Bucket-Assisted SampEn, have been proposed to eliminate unnecessary computations, the time demand restricts their use in real-time applications with long-term inputs. To address the time and space complexity issue in SampEn, we optimize Bucket-Assisted SampEn by dynamic memory allocation to avoid space complexity and accelerate the optimized Bucket-Assisted SampEn on Field Programmable Gate Arrays (FPGA). Our method accelerates Bucket-Assisted SampEn through efficient random storage and data access on FPGA. Furthermore, we introduce a scheduling strategy to handle unbalanced loads for time-intensive inter- and intra-similarity comparisons. We validate our approach on multi-source biomedical signals and demonstrate its effectiveness by achieving more than two orders of magnitude faster than straightforward defined SampEn computation. Our work provides a practical and effective approach for measuring time series complexity using Bucket-Assisted SampEn on FPGA, with the potential for real-time applications with long-term inputs.

Reference 
 
 
DOI ieeexplore