一种自适应的脑电信号噪声检测与去除方法

An Adaptive Method for Detecting and Removing EEG Noise

针对麻醉深度监测中脑电信号噪声的实时检测与去除的问题,提出了一种自适应的脑电信号噪声检测与去除方法。利用离散小波变换提取一段脑电信号的低频能量与高频能量,并针对信号的低频波段与高频波段,设置两组阈值,阈值能依据最近一段脑电信号的能量情况自适应地进行更新。最后根据低频能量与高频能量所在的范围,从而判断信号受干扰的等级与情况,并进行相应的去噪处理。结果表明,该方法能够较为准确地检测与去除脑电信号中的噪声干扰,并提高了计算的特征参数的稳定性。

To solve the problem of real-time detection and removal of EEG signal noise in anesthesia depth monitoring, we proposed an adaptive EEG signal noise detection and removal method. This method uses discrete wavelet transform to extract the low-frequency energy and high-frequency energy of a segment of EEG signals, and sets two sets of thresholds for the low-frequency band and high-frequency band of the EEG signal. These two sets of thresholds can be updated adaptively according to the energy situation of the most recent EEG signal. Finally, we judge the level of signal interference according to the range of low-frequency energy and high-frequency energy, and perform corresponding denoising processing. The results show that the method can more accurately detect and remove the noise interference in the EEG signal, and improve the stability of the calculated characteristic parameters.