样本熵及在脑电癫痫检测中的应用

The Sample Entropy and Its Application in EEG Based Epilepsy Detection

对癫痫进行检测和预报具有非常重要的临床意义。首先采用近似熵对癫痫患者的脑电信号进行分析，探索运用近似熵进行癫痫检测的可行性。针对近似熵存在的问题，选用一种与近似熵类似但精度更好的方法一样本熵，并同近似熵就在癫痫脑电信号中的应用进行了比较分析。结果显示癫痫发作时脑电信号的近似熵和样本熵均明显低于发作前和发作后。样本熵的变化幅度明显大于近似熵，样本熵的变化幅度相对于近似熵提高了约10％～25％。

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.