FAST WAVELET TRANSFORMATION OF EEG
FAST WAVELET TRANSFORMATION OF EEG
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DOI：
10.1016/0013-4694(94)90165-1
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发表时间：
1994-12-01
期刊：
影响因子：
--
通讯作者：
SATO, S
中科院分区：
文献类型：
--
作者：
SCHIFF, SJ;ALDROUBI, A;SATO, S
小波变换在脑电分析中具有傅立叶变换无法比拟的优势。最近的工作已经证明了小波的适用性的尖峰和癫痫发作检测，但计算的要求已经过度。我们比较质量的连续小波变换的特征提取使用标准的数值技术，更快速的算法，利用多项式样条和多分辨率框架。我们进一步对比过滤与不使用替代数据来模拟背景噪声之间的差异，展示了保留关键与冗余采样的特征提取，并与不同形状的小波进行分析。比较窗口傅立叶变换，类似地过滤，在不同的数据窗口长度。我们在这里报告了一个显着减少所需的计算时间进行这种分析，而不影响特征提取的准确性。现在看来，在普通微处理器上使用小波变换对EEG进行真实的实时滤波和分解在技术上是可行的。
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.