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.
Chen, C, Liu, C, Li, J & da Silva, B 2023, 'Acceleration of Bucket-Assisted Fast Sample Entropy for Biomedical Signal Analysis', IEEE Transactions on Instrumentation and Measurement, vol. 72, no. 99, 2007311, pp. 1-11. https://doi.org/10.1109/TIM.2023.3315412
Chen, C., Liu, C., Li, J., & da Silva, B. (2023). Acceleration of Bucket-Assisted Fast Sample Entropy for Biomedical Signal Analysis. IEEE Transactions on Instrumentation and Measurement, 72(99), 1-11. Article 2007311. https://doi.org/10.1109/TIM.2023.3315412
@article{935c98d11baf4bfea2328d9b05bd9f68,
title = "Acceleration of Bucket-Assisted Fast Sample Entropy for Biomedical Signal Analysis",
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.",
keywords = "Field-Programmable Gate Array (FPGA), High- Levels Synthesis (HLS, Information Entropy, Sample Entropy, Complexity Measurement, Biomedical Signals",
author = "Chao Chen and Chengyu Liu and Jianqing Li and {da Silva}, Bruno",
note = "Publisher Copyright: IEEE",
year = "2023",
month = sep,
day = "15",
doi = "10.1109/TIM.2023.3315412",
language = "English",
volume = "72",
pages = "1--11",
journal = "IEEE Transactions on Instrumentation and Measurement",
issn = "0018-9456",
publisher = "Institute of Electrical and Electronics Engineers",
number = "99",
}