Dikanev T, Smirnov D, Wennberg R, Velazquez J L Perez, Bezruchko B
Saratov State University, Institute of Radio Engineering and Electronics of Russian Academy of Sciences, 83, Saratov 410026, Saratov, Russia.
Clin Neurophysiol. 2005 Aug;116(8):1796-807. doi: 10.1016/j.clinph.2005.04.013.
The investigation of nonstationarity in complex, multivariable signals, such as electroencephalographic (EEG) recordings, requires the application of different and novel approaches to analysis. In this study, we have divided the EEG recordings during epileptic seizures into sequential stages using spectral and statistical analysis, and have as well reconstructed discrete-time models (maps) that reflect dynamical (deterministic) properties of the EEG voltage time series.
Intracranial human EEG recordings with epileptic seizures from three different subjects with medically intractable temporal lobe epilepsy were studied. The methods of statistical (power spectra, wavelet spectra, and one-dimensional probability distribution functions) and dynamical (comparison of dynamical models) nonstationarity analysis were applied.
Dynamical nonstationarity analysis revealed more detailed inner structure within the seizures than the statistical analysis. Three or four stages with different dynamics are typically present within seizures. The difference between interictal activity and seizure events was also more evident through dynamical analysis.
Nonstationarity analysis can reveal temporal structure within an epileptic seizure, which could further understanding of how seizures evolve. The method could also be used for identification of seizure onset.
Our approach reveals new information about the temporal structure of seizures, which is inaccessible using conventional methods.
研究复杂多变量信号（如脑电图（EEG）记录）中的非平稳性，需要应用不同的新颖分析方法。在本研究中，我们利用频谱和统计分析将癫痫发作期间的EEG记录划分为连续阶段，并重建了反映EEG电压时间序列动态（确定性）特性的离散时间模型（映射）。
研究了来自三名患有药物难治性颞叶癫痫的不同受试者的颅内癫痫发作EEG记录。应用了统计（功率谱、小波谱和一维概率分布函数）和动态（动态模型比较）非平稳性分析方法。
动态非平稳性分析揭示的发作内内部结构比统计分析更详细。发作期间通常存在三或四个具有不同动态的阶段。通过动态分析，发作间期活动与发作事件之间的差异也更明显。
非平稳性分析可以揭示癫痫发作内的时间结构，这有助于进一步理解发作如何演变。该方法还可用于识别发作起始。
我们的方法揭示了有关发作时间结构的新信息，这是传统方法无法获取的。
