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Sign series entropy analysis of short-term heart rate variability
  • ISSN号:1001-6538
  • 期刊名称:CHINESE SCIENCE BULLETIN
  • 时间:2009.12
  • 页码:4610-4615
  • 分类:TP181[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程] U491.254[交通运输工程—交通运输规划与管理;交通运输工程—道路与铁道工程]
  • 作者机构:[1]Key Laboratory of Modern Acoustics, Institute for Biomedical Electronic Engineering, Department of Electronic Science and Engi- neering, Nanjing University, Nanjing 210093, China, [2]College of Geography and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210003, China, [3]The Affiliated Drumtower Hospital of Nanjing University Medical School, Nanjing 210008, China
  • 相关基金:Supported by the National Natural Science Foundation of China (Grant Nos. 60501003, 60701002) and Colleges Oriented Provincial Natural Science Research Plan of Jiangsu Province (Grant No. 06KJD510138)
  • 相关项目:睡眠节律瞬态特性研究与缺省网络构建
中文摘要:

复杂性和非线性途径能被用来在心率可变性(HRV ) 学习时间、结构的顺序表明内在的规则和心血管的规定的生理的本质,它对理解有用。为临床的应用,对短期的 HRV 分析合适的方法是更珍贵的。在这篇论文,符号系列熵分析(SSEA ) 被建议描绘 HRV 的方向变化的特征。结果证明那个 SSEA 方法能从短期的 HRV 信号检测易感知地生理、病理学的变化,并且方法也显示出它的坚韧性到 nonstationarity 和噪音。因此,它为临床的 HRV 和另外的复杂生理的信号的分析作为一个有效方法被建议。

英文摘要:

Complexity and nonlinearity approaches can be used to study the temporal and structural order in heart rate variability (HRV) signal, which is helpful for understanding the underlying rule and physiological essence of cardiovascular regulation. For clinical applications, methods suitable for short-term HRV analysis are more valuable. In this paper, sign series entropy analysis (SSEA) is proposed to characterize the feature of direction variation of HRV. The results show that SSEA method can detect sensitively physiological and pathological changes from short-term HRV signals, and the method also shows its robustness to nonstationarity and noise. Thus, it is suggested as an efficient way for the analysis of clinical HRV and other complex physiological signals.

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