where L is the length of entropy, BSE(i) is the BSE extracted by BSEA, and SWIBSE(i) is the BSE extracted by SWIBSEA.
When the total number of PRV data points in buffer is less than window width, N < [N.sub.w], for BSEA, the BSE = 0 based on its theory; for SWIBSEA, this is the initial process, and we compute the entropy by iterative, and the value increases with N.
The time consumption of BSEA and SWIBSEA are 0.132 s and 4.769 s for the young subject and 0.192 s and 8.438 s for the old subject, respectively.
Comparison of BSEA and SWIBSEA under the Different Lengths of SSV.
The time consumption of SWIBSEA are from 0.182 s to 0.218 s, and that of BSEA are 42, 45, 45, 46, 50, 51, 62, 134, and 426 times for SWIB-SEA.
When m increases from 2 to 10, the time consumption of SWIBSEA are from 0.115 s to 0.137 s, and that of BSEA are 40, 42, 42, 44, 46, 49, 60, 126, and 388 times for SWIBSEA.
There are significant differences between BSEA and SWIBSEA (P < 0.001, two-sample t-test), and the increase of m does not affect the difference between the young and the elderly.
Comparison of BSEA and SWIBSEA under Different [N.sub.w, s] .
BSEA, as a nonlinear method, has been employed to HRV signal analysis.
In this study, on the basis of BSEA and with the theory of sliding window iterative, we proposed SWIBSEA for improving the computing efficiency of BSEA.
In this study, the sliding window iterative theory is used to improve the BSEA, and the SWIBSEA is proposed and employed to analyze the data of healthy young and old subjects from MIT/PhysioNet/Fantasia database.
Caption: FIGURE 3: The comparison of BSEA and SWIBSEA for a young subject (a) and an old subject (b), when a = 0.5, [N.sub.w] = 300, and m = 3.