Yuan Si-Nian, Li Ruo-Wei, Zhu Zi-Fu, Ma Sheng-Cai, Niu Hang-Duo, Ye Ji-Lun, Zhang Xu
Health Science Center, Biomedical Engineering, Shenzhen University, Shenzhen, 518060.
Shenzhen Key Lab for Biomedical Engineering, Shenzhen, 518060.
Zhongguo Yi Liao Qi Xie Za Zhi. 2022 May 30;46(3):248-253. doi: 10.3969/j.issn.1671-7104.2022.03.003.
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.
为解决麻醉深度监测中脑电信号噪声的实时检测与去除问题，我们提出了一种自适应脑电信号噪声检测与去除方法。该方法利用离散小波变换提取一段脑电信号的低频能量和高频能量，并为脑电信号的低频段和高频段设置两组阈值。这两组阈值可根据最新脑电信号的能量情况进行自适应更新。最后，根据低频能量和高频能量的范围判断信号干扰程度，并进行相应的去噪处理。结果表明，该方法能够更准确地检测和去除脑电信号中的噪声干扰，提高计算得到的特征参数的稳定性。
