摘要
自1948年信息论之父C.E.Shannon提出"信息熵"的概念后,熵测度用于生理信号复杂度分析得到众多研究者的支持和青睐。其后在"信息熵"理论的指导下,出现了Kolmogorov熵、近似熵、动态近似熵、样本熵、模式熵、多尺度熵、基本尺度熵、联合熵、模糊近似熵、模糊测度熵等多种熵测度算法,极大提升了生物医学信号分析水平。本文系统综述了熵理论的发展历史及在生物医学信号分析中的作用,归纳出熵理论的4个历史阶段:起源、发展、繁荣和现状,并详细分析了每一阶段各种熵测度算法产生的原因、结果及存在的问题。
Since C.E. Shannon proposed the ＂information entropy＂ in 1948, the entropy measures have achieved encouraging progresses in the complexity analysis for biomedical signals. Later with the guidance of ＂ information entropy＂, many measures, such as Kolmogorov entropy, approximate entropy, dynamic approximate entropy, sample entropy, mode entropy, muhiscale entropy, base scale entropy, joint entropy, fuzzy entropy, fuzzy measure entropy, were proposed and they greatly promoted the level of biomedical signal analysis. In this paper, the history of entropy theory and its role in biomedical signal analysis were reviewed. This paper systematically summarized the four development phases of entropy theory： origin, development, prosperity and status quo. Meanwhile, there was also a detailed analysis for the reasons and defects of each entropy algorithm.
出处
《北京生物医学工程》
2012年第5期539-543,共5页
Beijing Biomedical Engineering
基金
国家自然科学基金青年基金(61201049)
中国博士后科学基金(20110491593)资助
关键词
熵理论
生物医学信号
非线性分析
entropy theory
biomedical signal
nonlinear analysis
