CAJ | 학술논문

为消除混杂在脑电信号( EEG)中的噪声，提出一种基于双密度小波邻域相关阈值处理的EEG消噪方法。利用双密度小波对EEG分解，得到多层的信号高频系数。根据小波系数的局部统计依赖性，运用邻域相关阈值处理算法进行收缩，将收缩后的小波系数进行重构得到消噪后的信号。对加噪标准信号和实测EEG的消噪实验结果表明，与一代离散小波和传统软阈值法相比，信噪比、均方根误差和最大误差3个消噪效果评价指标都有明显改善。
위소제혼잡재뇌전신호( EEG)중적조성，제출일충기우쌍밀도소파린역상관역치처리적EEG소조방법。이용쌍밀도소파대EEG분해，득도다층적신호고빈계수。근거소파계수적국부통계의뢰성，운용린역상관역치처리산법진행수축，장수축후적소파계수진행중구득도소조후적신호。대가조표준신호화실측EEG적소조실험결과표명，여일대리산소파화전통연역치법상비，신조비、균방근오차화최대오차3개소조효과평개지표도유명현개선。
To eliminate the noise mixed in Electroencephalogram ( EEG ) , an EEG de-noising method is proposed based on double-density discrete wavelet transform using neighbor-dependency thresholding. Firstly, high frequency coefficients of multilayer signals are obtained by double-density discrete wavelet decomposition. Then, the wavelet coefficients are shrunk with neighbor-dependency thresholding algorithm, which takes the statistical dependencies of the wavelet coefficients into account. Finally, the de-noising signal is obtained by reconstructing shrunk wavelet coefficients. The simulation results of the de-noising experiments on standard noise-adding signal and real EEG show that compared to the first generation discrete wavelet algorithm and traditional soft threshold methods, the proposed de-noising algorithm has the benefits of higher SNR, lower RMSE and Errmax .