摘要
为了去除神经细胞外单细胞动作电位(即锋电位)记录信号中的各种噪声,提高幅值很小的单细胞锋电位信号检测的正确性,根据多通道微电极阵列记录信号中各个通道之间噪声空间相关性较强的特点,提出主成分分析(PCA)去噪与小波阈值去噪相结合的联合去噪方法.采用PCA方法提取并去除多通道记录信号中相关噪声的第一主成分,然后将信号进行小波多尺度分解,采用软阈值法去除各尺度下的噪声.仿真数据和测试结果表明,联合去噪方法可以同时去除有色噪声和白噪声,在各通道锋电位序列相互独立而噪声相关性较强的情况下,可以显著提高锋电位信号的信噪比.联合去噪方法的性能明显优于PCA去噪方法和小波阈值去噪方法单独使用时的性能,是一种有效的多通道锋电位信号去噪新方法.
Based on the fact that there are strong spatial correlations among the noises recorded in multi-channels in a microelectrode array,a new denoising method was developed by combining principle component analysis （PCA） with wavelet threshold method,in order to eliminate various types of noises in extracellular single neuronal action potential （i.e.spike） recordings.The largest noise component in the multi-channel recording signals was first extracted by using PCA decomposition and was removed from the raw signals.The signals then went through wavelet multi-level decomposition.The residual noises in every wavelet level were removed by a soft-thresholding method.Both simulation data and experimental recordings were used to test this PCA-wavelet combined algorithm.The results showed that the algorithm can simultaneously suppress white noise and colored noise,and significantly increase the signal-to-noise ratio of spike signals.Especially for the multiple channel recordings with independent spike signals and highly correlated noises,the performance of the PCA-wavelet combined algorithm significantly surmounts the individual performance of PCA denoising and wavelet threshold denoising used separately.Therefore,the novel PCA-wavelet combined algorithm provides an effective and useful method to denoise multichannel spike signals.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2010年第1期104-110,共7页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(30570585
30770548)
关键词
锋电位
主成分分析
小波阈值去噪
信噪比
微电极阵列
spike
principal component analysis
wavelet threshold denoising
signal-to-noise ratio
microelectrode array
