文章基本信息

标题：Evaluation of Detrending Method Based on Ensemble Empirical Mode Decomposition for HRV Analysis
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作者：Zeng, Chao ; Xu, Xiaowen
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2014
卷号：9
期号：6
页码：1325-1332
DOI：10.4304/jcp.9.6.1325-1332
语种：English
出版社：Academy Publisher
摘要：Heart rate variability (HRV) is a key indicator for assessing autonomous nervous system activity. Because nonstationary and slow trends which can cause distortion to HRV analysis are usually occurred in HRV signals, detrending scheme is necessary before HRV analysis. Ensemble empirical mode decomposition (EEMD), which offers the ability to break down signals into a set of intrinsic mode functions and acts as a high-pass filter through partial reconstruction, is proposed for HRV detrending. Experiment results show that the detrending method based on EEMD can achieve better performance than the smoothing priors approach (SPA), which is one of the most widely used method.
关键词：Ensemble Empirical Mode Decomposition;Heart Rate Varibility;Detrending;Smoothing Priors Approach