[样本熵及其在基于脑电图的癫痫检测中的应用]
[The sample entropy and its application in EEG based epilepsy detection].
作者信息
Bai Dongmei, Qiu Tianshuang, Li Xiaobing
机构信息
Department of Electronic Engineering, Dalian University of Technology, Dalian 116024, China.
出版信息
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2007 Feb;24(1):200-5.
It is of great importance for the detection of epilepsy in clinical applications. Based on the limitations of the common used approximate entropy (ApEn) in the epilepsy detection, this paper analyzes epileptic EEG signals with the sample entropy (SampEn) approach, a new method for signal analysis with much higher precision than that of the ApEn. Data analysis results show that the values from both ApEn and SampEn decrease significantly when the epilepsy is burst. Furthermore, the SampEn is more sensitive to EEG changes caused by the epilepsy, about 15%-20% higher than the results of the ApEn.
这在临床应用中对癫痫的检测非常重要。基于常用近似熵（ApEn）在癫痫检测中的局限性，本文采用样本熵（SampEn）方法分析癫痫脑电信号，SampEn是一种信号分析的新方法，其精度比ApEn高得多。数据分析结果表明，癫痫发作时ApEn和SampEn的值均显著下降。此外，SampEn对癫痫引起的脑电变化更敏感，比ApEn的结果高约15%-20%。
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