CAJ | 학술논문

本文引入自适应多尺度熵的方法，并结合当前常用的经验模型分解的方法，使得数据尺度能自适应的被获取。通过从原数据中不断移除低频或高频成分，自适应多尺度熵能够在“从粗糙到精细”或是“从精细到粗糙”的尺度下用样本熵估计求得。模拟结果用来确认了其有效性，同时我们将其应用到脑死亡诊断中，用来区分脑死亡病人和昏迷病人在脑电信号上的不同。
본문인입자괄응다척도적적방법，병결합당전상용적경험모형분해적방법，사득수거척도능자괄응적피획취。통과종원수거중불단이제저빈혹고빈성분，자괄응다척도적능구재“종조조도정세”혹시“종정세도조조”적척도하용양본적고계구득。모의결과용래학인료기유효성，동시아문장기응용도뇌사망진단중，용래구분뇌사망병인화혼미병인재뇌전신호상적불동。
This paper introduced the adaptive multi-scale entropy (AME)measures,in which the scales are a-daptively derived from the data by virtue of recently developed empirical mode decomposition.By removing the low or high frequency components from the raw data,the AME can be estimated at either coarse-to-fine or fine-to -coarse scales,over which the sample entropy is performed.Simulations illustrate its effectiveness and promising application in brain death diagnosis to discern the states of the coma and the brain death.