广东工业大学学报 ›› 2019, Vol. 36 ›› Issue (03): 32-38.doi: 10.12052/gdutxb.180161

• 综合研究 • 上一篇    下一篇

基于改进的多时间尺度特征排列熵的心率变异性分析研究

雷瑞生, 凌永权   

  1. 广东工业大学 信息工程学院, 广东 广州 510006
  • 收稿日期:2018-12-07 出版日期:2019-05-09 发布日期:2019-04-04
  • 通信作者: 凌永权(1973-),男,教授,博士,博士生导师,主要研究方向为最优化理论研究与时频分析等.E-mail:yongquanling@gdut.edu.cn E-mail:yongquanling@gdut.edu.cn
  • 作者简介:雷瑞生(1993-),男,硕士研究生,主要研究方向为医疗信号处理、机器学习、数据挖掘.
  • 基金资助:
    国家自然科学基金资助项目（U1701266，61372173，61471132，61671163）；广东省自然科学基金资助项目（2014A030310346）；广东省科技计划资助项目（2015A030401090）

A Heart Rate Variability Analysis via Modified Multi-time Scale Permutation Entropy

Lei Rui-sheng, Ling Bingo Wing-Kuen   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2018-12-07 Online:2019-05-09 Published:2019-04-04

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摘要： 为了分析不同生理状态下人体心率变异性的差异性，并弥补传统心率变异性分析方法时间尺度特征单一的不足，本文结合了互补式集合平均经验模态分解和改进的排列熵算法对心率变异时间序列进行了具有多时间尺度特征的排列熵分析，并计算得到反映心率变异显著差异性的指标CEEMD-mPE.最后基于MIT-BIH心率失常数据库的实验结果分析表明，本文所使用的心率变异性分析方法相比于其他基于熵的分析方法具有更优异的表现.

关键词: 互补式集合平均经验模态分解, 本征模态函数, 排列熵, 心率变异性

Abstract: For analyzing the differences in human heart rate variability under different physiological conditions, and overcoming the problem of analyzing with only single time scale in traditional heart rate variability analysis methods, an HRV analysis method combining the complementary ensemble empirical mode decomposition and modified permutation entropy algorithm is proposed to obtain an indicator named CEEMD-mPE for measuring the obvious differences from HRV time series. The experimental results based on the MIT-BIH arrhythmia database show that our proposed HRV analysis method outperforms the other existing methods based on entropy.

Key words: complementary ensemble empirical mode decomposition, intrinsic mode functions, permutation entropy, heart rate variability

中图分类号: 

  • TP301
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