Conlon T, Ruskin H J, Crane M
Dublin City University, Dublin 9, Ireland.
Comput Biol Med. 2009 Sep;39(9):760-7. doi: 10.1016/j.compbiomed.2009.06.003. Epub 2009 Jul 5.
The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on the recent use of multivariate techniques to study the cross-correlation dynamics between electroencephalographic (EEG) channels. The maximum overlap discrete wavelet transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency-dependent changes in the correlation structure between channels which may be indicative of seizure activity. The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency-dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics.
癫痫发作的特征描述有助于针对性药物癫痫预防技术的设计和术前评估。在本文中，我们详述了近期使用多变量技术来研究脑电图（EEG）通道之间的互相关动力学。应用最大重叠离散小波变换（MODWT）以便将EEG通道分离为其潜在频率。然后根据特征谱分析每个频率下通道之间互相关矩阵的动力学。通过对特征谱的检查，我们表明有可能识别通道之间相关性结构中与频率相关的变化，这些变化可能指示癫痫发作活动。该技术应用于EEG癫痫样数据，结果表明相关动力学随时间和频率而变化，高频时通道之间的相关性更大。此外，发现小波能量重新分布，分数能量增加表明癫痫发作期间高频的相对重要性。癫痫发作期间低频时相关性和能量也会发生动态变化，这表明监测与频率相关的相关性结构可以表征这些期间EEG信号的变化。未来的工作将涉及研究其他大特征值和频率间相关性，以确定额外的癫痫发作特征。
