An EMD based method for detrending RR interval series without resampling  

作　　者：曾超 蒋奇云 陈朝阳 徐敏 

机构地区：[1]School of Geosciences and Info-physics, Central South University [2]College of Information Science and Technology, Shihezi University [3]Department of Biomedical Engineering, Wayne State University

出　　处：《Journal of Central South University》2015年第2期567-574,共8页中南大学学报（英文版）

基　　金：Project(41227803)supported by the National Natural Science Foundation of China;Project(KF11011)supported by the State Key Laboratory of Automotive Safety and Energy(Tsinghua University),China;Project(DTNH22-08-C-00082)supported by the National Highway Traffic Safety Administration,USA

摘　　要：Slow trends in the RR interval(RRI) series should be removed in the preprocessing step to get a reliable result of heart rate variability(HRV) analysis. Re-sampling is required to convert the unevenly sampled RRI series into evenly sampled time series when using the widely accepted smoothness priors approach(SPA). Noise is introduced in this process and the information quality is thus compromised. Empirical mode decomposition(EMD) and its variants, were introduced to directly process the unevenly sampled RRI series. Besides, a RR interval model was proposed to fascinate the introduction of standard metrics for the evaluation of the detrending performance. Based on standard metrics including signal-to-noise-ratio in d B(ISNR), mean square error(EMS), and percent root square difference(DPRS), the effectiveness of detrending methods in RR interval analysis were determined. Results demonstrate that complementary ensemble EMD(CEEMD, a variant of EMD) based method has a higher ISNR, a lower EMS and a lower DPRS as well as a better RRI series detrending performance compared with the SPA method, which would in turn lead to a more accurate HRV analysis.Slow trends in the RR interval（RRI） series should be removed in the preprocessing step to get a reliable result of heart rate variability（HRV） analysis. Re-sampling is required to convert the unevenly sampled RRI series into evenly sampled time series when using the widely accepted smoothness priors approach（SPA）. Noise is introduced in this process and the information quality is thus compromised. Empirical mode decomposition（EMD） and its variants, were introduced to directly process the unevenly sampled RRI series. Besides, a RR interval model was proposed to fascinate the introduction of standard metrics for the evaluation of the detrending performance. Based on standard metrics including signal-to-noise-ratio in d B（ISNR）, mean square error（EMS）, and percent root square difference（DPRS）, the effectiveness of detrending methods in RR interval analysis were determined. Results demonstrate that complementary ensemble EMD（CEEMD, a variant of EMD） based method has a higher ISNR, a lower EMS and a lower DPRS as well as a better RRI series detrending performance compared with the SPA method, which would in turn lead to a more accurate HRV analysis.

关 键 词：heart rate variability empirical mode decomposition DETRENDING RR interval model 

分 类 号：O212.1[理学—概率论与数理统计]