摘要
本文应用小波变换方法从背景噪声中提取出脑干听觉诱发电位(BAEP)信号并进行特征识别。首先我们讨论了母小波和小波变换算法的选择,发现双正交母小波bior5.5和稳定离散小波变换(SWT)最适合BAEP信号的小波多分辨分析。通过D6尺度小波系数的相关性分析,发现相关性大于0.4的单次刺激记录具有较高的信噪比,使得仅数次刺激的叠加平均就能清晰地识别出BAEP信号的各个波。最后我们用此方法来挑选各次刺激记录,对每10次记录进行叠加平均和小波滤波结合消噪,并正确识别和计算出BAEP信号各个波的潜伏期。实验证明本方法通过小波系数相关性分析能有效选取单次刺激的BAEP记录,在大大减少刺激次数的同时,达到了更好的消噪效果。
We proposed a multi-resolution-wavelet-transform based method to extract brainstem auditory evoked potential（BAEP）from the background noise and then to identify its characteristics correctly.Firstly we discussed the mother wavelet and wavelet transform algorithm and proved that bi-orthogonal wavelet bior5.5and stationary discrete wavelet transform（SWT）were more suitable for BAEP signals.The correlation analysis of D6 scale wavelet coefficients between single trails and the ensemble average of all trails showed that the trails with good correlation（〉0.4）had higher signal-to-noise ratio,so that we could get a clear BAEP from a few trails by an average and wavelet filter method.Finally,we used this method to select desirable trails,extracted BAEP from every 10 trails and calculated theⅠ-Ⅴinter-waves＇ latency.The results showed that this strategy of trail selection was efficient.This method can not only achieve better de-noising effect,but also greatly reduce the stimulation time needed as well.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2015年第3期514-519,共6页
Journal of Biomedical Engineering
关键词
脑干听觉诱发电位
小波变换
相关性分析
brainstem auditory evoked potential
wavelet transformation
correlation analysis
