Base scale entropy analysis (BSEA) is a nonlinear method to analyze heart rate variability (HRV) signal. However, the time\nconsumption of BSEA is too long, and it is unknown whether the BSEA is suitable for analyzing pulse rate variability (PRV)\nsignal. Therefore, we proposed a method named sliding window iterative base scale entropy analysis (SWIBSEA) by combining\nBSEA and sliding window iterative theory. The blood pressure signals of healthy young and old subjects are chosen from the\nauthoritative international database MIT/PhysioNet/Fantasia to generate PRV signals as the experimental data. Then, the BSEA\nand the SWIBSEA are used to analyze the experimental data; the results show that the SWIBSEA reduces the time consumption\nand the buffer cache space while it gets the same entropy as BSEA. Meanwhile, the changes of base scale entropy (BSE) for\nhealthy young and old subjects are the same as that of HRV signal. Therefore, the SWIBSEA can be used for deriving some\ninformation from long-term and short-term PRV signals in real time, which has the potential for dynamic PRV signal analysis\nin some portable and wearable medical devices.
Loading....