Title: A fast approximate entropy algorithm for heart rate variability analysis

Authors: Haiping Yang; Lijuan Chou; Yongxin Chou; Jicheng Liu

Addresses: School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China ' School of Computer and Information Technology, Northeast Petroleum University, Daqing, China ' School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China ' School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, China

Abstract: Approximate entropy is widely used in medical biological signal processing. However, due to the high complexity and time-consumption of approximate entropy calculation, it is generally only used for offline signal processing. In this study, the calculation process of approximate entropy is optimised. The goal is to shorten the running time of the algorithm without changing the approximate entropy value. The correctness and timeliness of the improved algorithm are verified by random number, and the improved algorithm is applied to HRV signal. Simulation results show that the improved algorithm can shorten the running time by 27-99 times, and the longer the cache length, the better the improvement effect. This makes it possible to process real-time biological signals with approximate entropy.

Keywords: approximate entropy; microprocessor processing; electrocardiograms; ECG; heart rate variability; HRV; entropy; short-time HRV; sliding window iteration; online processing.

DOI: 10.1504/IJMIC.2024.135554

International Journal of Modelling, Identification and Control, 2024 Vol.44 No.1, pp.67 - 76

Received: 26 Aug 2022
Received in revised form: 26 Nov 2022
Accepted: 24 Dec 2022
Published online: 18 Dec 2023 *

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