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A fast sample entropy for pulse rate variability analysis

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Abstract

Sample entropy is an effective nonlinear index for analyzing pulse rate variability (PRV) signal, but it has problems with a large amount of calculation and time consumption. Therefore, this study proposes a fast sample entropy calculation method to analyze the PRV signal according to the microprocessor process of data updating and the principle of sample entropy. The simulated data and PRV signal are employed as experimental data to verify the accuracy and time consumption of the proposed method. The experimental results on simulated data display that the proposed improved sample entropy can improve the operation rate of the entropy value by a maximum of 47.6 times and an average of 28.6 times and keep the entropy value unchanged. Experimental results on PRV signal display that the proposed improved sample entropy has great potential in the real-time processing of physiological signals, which can increase approximately 35 times.

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Funding

This work was supported by the National Natural Science Foundation of China (grant 61901062), and the Outstanding Young Backbone Teachers of the ‘Blue Project’ in Colleges and Universities.

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Authors and Affiliations

  1. School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China

    Lijuan Chou, Haiping Yang, Jicheng Liu & Yongxin Chou

  2. School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China

    Lijuan Chou & Shengrong Gong

  3. School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China

    Shengrong Gong

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  1. Lijuan Chou
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  2. Shengrong Gong
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  5. Yongxin Chou
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Correspondence to Yongxin Chou.

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Chou, L., Gong, S., Yang, H. et al. A fast sample entropy for pulse rate variability analysis. Med Biol Eng Comput 61, 1603–1617 (2023). https://doi.org/10.1007/s11517-022-02766-y

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