采样参数变化对脑电信号复杂度分析的影响

Effects of Sampling Parameter Variation on the Complexity Analysis of EEG

计算和分析了脑电的算法复杂度和近似熵随着信号的数据点数、采样频率和时间的变化过程。结果说明 :采样频率一定时 ,数据点数较大时计算所得的两种复杂度值较稳定 ;时间或数据点数一定时 ,可以采用较低的采样频率 ,便于各类信号之间的区分 。

The algorithmic complexity and the approximate entropy of EEG were calculated and analyzed with different data points, different sample frequencies and different sample time duration. The results showed that under fixed sample frequency, the longer the data was, the more stable the complexity values were. With fixed sample time duration or fixed data point, lower sample frequency would be better both for EEG distinguishing and for computing time saving.