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中文摘要：
基于希尔伯特-黄变换（HHT）理论,依据广义信息熵的概念,提出基于HHT边际谱熵和能量谱熵的概念和熵分析方法。对常规信号和混沌时间序列信号进行复杂性研究,结果表明本方法在刻画信号复杂度变化、抗脉冲干扰方面优于Lempel-Ziv复杂度和功率谱熵方法。将其应用于MIT-BIH标准数据库的实际心率变异（HRV）信号分析,结果显示HHT边际谱熵和能量谱熵能从HRV信号中敏感地检测出生理和病理状态的变化,统计学分析优于传统的功率谱熵方法,为临床HRV信号及其他复杂生理信号的分析提供一种有效的分析方法。
英文摘要：
According to the concept of generalized information entropy and Hilbert-Huang transform（ HHT） theory,the analysis method of heart rate variability signal was proposed based on the HHT marginal spectrum entropy and energy spectrum entropy.The complexity analysis was processed for the conventional signal and chaotic time series.The results showed that the method was superior to the method of the Lempel-Ziv complexity and the power spectrum entropy in depicting signal complexity and anti-pulse interference.Applying the new approach to actual heart rate variability signal（HRV） of the MIT-BIH standard database,it was demonstrated that this method could detect the physiological and pathological changes in HRV better than the traditional power spectrum entropy method,providing an effective analysis method for clinical HRV signal and other complex physiological signal.
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