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Journal of Healthcare Engineering
Volume 2017, Article ID 7406896, 10 pages
https://doi.org/10.1155/2017/7406896
Research Article

A Real-Time Analysis Method for Pulse Rate Variability Based on Improved Basic Scale Entropy

1School of Electrical and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
2Changshu No. 1 People’s Hospital, Changshu, China
3State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110014, China

Correspondence should be addressed to Yongxin Chou; moc.361@xyuohctul

Received 17 November 2016; Revised 18 February 2017; Accepted 7 March 2017; Published 9 May 2017

Academic Editor: Valentina Camomilla

Copyright © 2017 Yongxin Chou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.