摘要
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.
This paper introduced the adaptive multi-scale entropy （AME） measures, in which the scales are adaptively 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-tocoarse 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.
出处
《动力学与控制学报》
2014年第1期74-78,共5页
Journal of Dynamics and Control
基金
国家自然科学基金资助项目(11232005)
教育部博士点基金资助项目(20120074110020)~~
关键词
脑电信号
脑死亡诊断
自适应多尺度熵
样本熵
EEG signal, brain death diagnosis, adaptive multi-scale entropy, sample entropy
