Schiff S J, Aldroubi A, Unser M, Sato S
Department of Neurosurgery, Children's National Medical Center, Washington, DC 20010.
Electroencephalogr Clin Neurophysiol. 1994 Dec;91(6):442-55. doi: 10.1016/0013-4694(94)90165-1.
Wavelet transforms offer certain advantages over Fourier transform techniques for the analysis of EEG. Recent work has demonstrated the applicability of wavelets for both spike and seizure detection, but the computational demands have been excessive. We compare the quality of feature extraction of continuous wavelet transforms using standard numerical techniques, with more rapid algorithms utilizing both polynomial splines and multiresolution frameworks. We further contrast the difference between filtering with and without the use of surrogate data to model background noise, demonstrate the preservation of feature extraction with critical versus redundant sampling, and perform the analyses with wavelets of different shape. Comparison is made with windowed Fourier transforms, similarly filtered, at different data window lengths. We here report a dramatic reduction in computational time required to perform this analysis, without compromising the accuracy of feature extraction. It now appears technically feasible to filter and decompose EEG using wavelet transforms in real time with ordinary microprocessors.
与傅里叶变换技术相比，小波变换在脑电图（EEG）分析中具有某些优势。最近的研究表明小波在尖峰和癫痫发作检测方面的适用性，但计算需求过高。我们将使用标准数值技术的连续小波变换的特征提取质量与利用多项式样条和多分辨率框架的更快算法进行比较。我们进一步对比了使用和不使用替代数据来模拟背景噪声进行滤波之间的差异，展示了关键采样与冗余采样时特征提取的保留情况，并使用不同形状的小波进行分析。在不同数据窗口长度下，与经过类似滤波的加窗傅里叶变换进行比较。我们在此报告，进行此分析所需的计算时间大幅减少，同时不影响特征提取的准确性。现在看来，使用普通微处理器实时利用小波变换对脑电图进行滤波和分解在技术上是可行的。
