School of Electrical and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China.
Changshu No. 1 People's Hospital, Changshu, China.
J Healthc Eng. 2017;2017:7406896. doi: 10.1155/2017/7406896. Epub 2017 May 9.
Base scale entropy analysis (BSEA) is a nonlinear method to analyze heart rate variability (HRV) signal. However, the time consumption of BSEA is too long, and it is unknown whether the BSEA is suitable for analyzing pulse rate variability (PRV) signal. Therefore, we proposed a method named sliding window iterative base scale entropy analysis (SWIBSEA) by combining BSEA and sliding window iterative theory. The blood pressure signals of healthy young and old subjects are chosen from the authoritative international database MIT/PhysioNet/Fantasia to generate PRV signals as the experimental data. Then, the BSEA and the SWIBSEA are used to analyze the experimental data; the results show that the SWIBSEA reduces the time consumption and the buffer cache space while it gets the same entropy as BSEA. Meanwhile, the changes of base scale entropy (BSE) for healthy young and old subjects are the same as that of HRV signal. Therefore, the SWIBSEA can be used for deriving some information from long-term and short-term PRV signals in real time, which has the potential for dynamic PRV signal analysis in some portable and wearable medical devices.
基尺度熵分析（BSEA）是一种分析心率变异性（HRV）信号的非线性方法。然而，BSEA 的时间消耗过长，并且尚不清楚 BSEA 是否适合分析脉搏率变异性（PRV）信号。因此，我们提出了一种将 BSEA 和滑动窗口迭代理论相结合的方法，称为滑动窗口迭代基尺度熵分析（SWIBSEA）。从权威的国际数据库 MIT/PhysioNet/Fantasia 中选择健康年轻和老年受试者的血压信号来生成 PRV 信号作为实验数据。然后，使用 BSEA 和 SWIBSEA 来分析实验数据；结果表明，SWIBSEA 减少了时间消耗和缓冲区缓存空间，同时获得了与 BSEA 相同的熵。同时，健康年轻和老年受试者的基尺度熵（BSE）变化与 HRV 信号的变化相同。因此，SWIBSEA 可用于实时从长期和短期 PRV 信号中提取一些信息，这对于一些便携式和可穿戴医疗设备中的动态 PRV 信号分析具有潜力。
