中国组织工程研究

中国组织工程研究 ›› 2010, Vol. 14 ›› Issue (52): 9781-9785.doi: 10.3969/j.issn.1673-8225.2010.52.023

• 数字化骨科 digital orthopedics • 上一篇    下一篇

改进排列熵方法及其在心率变异复杂度分析中的应用

马千里1，卞春华2   

  1. 1南京邮电大学地理与生物信息学院，图像处理与图像通信江苏省重点实验室，江苏省南京市    210003；2南京大学电子科学与工程学院，生物医学电子工程研究所，江苏省南京市    210093
  • 出版日期:2010-12-24 发布日期:2010-12-24
  • 通讯作者: 卞春华，博士，副教授，硕士生导师，南京大学电子科学与工程学院，生物医学电子工程研究所，江苏省南京市 210093 bch@nju.edu.cn
  • 作者简介:马千里☆，男，1978年生，安徽省淮南市人，汉族，2006年南京大学毕业，博士，硕士生导师，主要从事信号与信息处理研究。 maql@njupt.edu.cn
  • 基金资助:

    国家自然科学基金项目(60501003)：非线性多参数智能模型用于心脏疾病临床诊断的研究。

Application of modified permutation entropy in heart rate variability analysis

Ma Qian-li1, Bian Chun-hua2   

  1. 1 Key Laboratory of Image Processing & Image Communication of Jiangsu Province, School of Geography and Biomedical Information, Nanjing University of Posts & Telecommunications, Nanjing  210003, Jiangsu Province, China; 2 Institute for Biomedical Electronic Engineering, School of Electronic Science and Engineering, Nanjing University, Nanjing  210093, Jiangsu Province, China
  • Online:2010-12-24 Published:2010-12-24
  • Contact: Bian Chun-hua, Doctor, Associate professor, Master’s supervisor, Institute for Biomedical Electronic Engineering, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, Jiangsu Province, China bch@nju.edu.cn
  • About author:Ma Qian-li☆, Doctor, Master’s supervisor, Key Laboratory of Image Processing & Image Communication of Jiangsu Province, School of Geography and Biomedical Information, Nanjing University of Posts & Telecommunications, Nanjing 210003, Jiangsu Province, China maql@njupt.edu.cn
  • Supported by:

    the National Natural Science Foundation of China, No. 60501003*

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摘要：

背景：心率变异信号蕴含着有关心血管系统调节的重要信息，很多非线性动力学方法和复杂性测度已被用于心率变异信号的分析。排列熵是近年提出的一种新的熵测度，具有概念简单，计算简洁等优点，在很多领域得到了广泛的应用。
目的：针对心率变异信号特点，对排列熵方法进行等值状态处理的改进，在排列符号序列中以相同符号代表等值状态；通过临床数据实验，考察改进排列熵方法在心率变异性信号分析中的应用价值。
方法：心率变异信号取自PhysioNet中MIT-BIH Fantasia数据库和BIDMC Congestive Heart Failure(CHF)数据库，分为年轻健康人、老年健康人和充血性心力衰竭患者3组。应用排列熵和改进排列熵方法对3组数据分别进行分析，通过单因素方差分析和t检验对结果进行统计分析。
结果与结论：排列熵的两种等值状态处理方法均无法对3组数据作出有效区分，尤其是年轻健康人和老年健康人两组数据；而改进排列熵方法可以对3组数据作出非常有效的区分，即使是在短时的心率变异性分析中(500个RR间期，6~7 min)。提示改进排列熵方法可以有效提高生理、病理心率变异信号的区分度，比排列熵方法更有效的表征心率变异信号的复杂性。

关键词: 心电图, 心率变异性, 衰老, 充血性心力衰竭, 复杂性, 排列熵

Abstract:

BACKGROUND: Heart rate variability (HRV) contains important information about regulation of cardiovascular system. Various nonlinear dynamics methods and complexity measure have been used for HRV analysis. Recently-proposed permutation entropy is a new complexity measure, which has been widely used due to its conceptual and computational simplicity.
OBJECTIVE: To improve the equal-state processing method of permutation entropy based on the characteristics of HRV using same symbol in the permutation symbolic series to represent the equal-states, and to investigate the value of the modified permutation entropy applied to the analysis of HRV via clinically collected data.
METHODS: HRV data were from MIT-BIH Fantasia database and BIDMC Congestive Heart Failure (CHF) database on PhysioNet. The databases consisted of three groups, i.e. healthy young, healthy elderly and CHF patients. The data were analyzed using permutation entropy and modified permutation entropy methods. One-way analysis of variance and t-test were utilized for statistical analysis.
RESULTS AND CONCLUSION: Both types of the equal-state processing methods of permutation entropy were failed to effectively distinguish three groups, especially between healthy young and healthy elderly groups. In contrast, the modified permutation entropy was able to distinguish three groups effectively, even when applied to short-term heart rate variability data (500 RR intervals, approximately 6-7 minutes). Modified permutation entropy can greatly improve the ability to distinguish the HRV signal under different physiological and pathological conditions. It can characterize the complexity of HRV more effectively than permutation entropy.

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