Thakor N V, Guo X R, Sun Y C, Hanley D F
Biomedical Engineering Department, Johns Hopkins School of Medicine, Baltimore, MD 21205.
IEEE Trans Biomed Eng. 1993 Nov;40(11):1085-94. doi: 10.1109/10.245625.
Neurological injury, such as from cerebral hypoxia, appears to cause complex changes in the shape of evoked potential (EP) signals. To characterize such changes we analyze EP signals with the aid of scaling functions called wavelets. In particular, we consider multiresolution wavelets that are a family of orthonormal functions. In the time domain, the multiresolution wavelets analyze EP signals at coarse or successively greater levels of temporal detail. In the frequency domain, the multiresolution wavelets resolve the EP signal into independent spectral bands. In an experimental demonstration of the method, somatosensory EP signals recorded during cerebral hypoxia in anesthetized cats are analyzed. Results obtained by multiresolution wavelet analysis are compared with conventional time-domain analysis and Fourier series expansions of the same signals. Multiresolution wavelet analysis appears to be a different, sensitive way to analyze EP signal features and to follow the EP signal trends in neurologic injury. Two characteristics appear to be of diagnostic value: the detail component of the MRW displays an early and a more rapid decline in response to hypoxic injury while the coarse component displays an earlier recovery upon reoxygenation.
神经损伤，如脑缺氧导致的损伤，似乎会使诱发电位（EP）信号的形状发生复杂变化。为了描述此类变化，我们借助称为小波的缩放函数来分析EP信号。具体而言，我们考虑多分辨率小波，它是一族正交函数。在时域中，多分辨率小波在粗略或时间细节逐渐增加的水平上分析EP信号。在频域中，多分辨率小波将EP信号分解为独立的频谱带。在该方法的实验演示中，分析了在麻醉猫脑缺氧期间记录的体感EP信号。将多分辨率小波分析获得的结果与相同信号的传统时域分析和傅里叶级数展开进行比较。多分辨率小波分析似乎是一种不同的、敏感的分析EP信号特征以及跟踪神经损伤中EP信号趋势的方法。两个特征似乎具有诊断价值：多分辨率小波的细节分量在对缺氧损伤的反应中显示出早期且更快的下降，而粗略分量在复氧时显示出更早的恢复。
