Schiff N D, Victor J D, Canel A, Labar D R
Department of Neurology and Neuroscience, New York Hospital-Cornell Medical Center, NY 10021, USA.
Biol Cybern. 1995;72(6):519-26. doi: 10.1007/BF00199894.
We describe a method for the characterization of electroencephalographic (EEG) signals based on a model which features nonlinear feedback. The characteristic EEG 'fingerprints' obtained through this approach display the time-course of nonlinear interactions, rather than aspects susceptible to standard spectral analysis. Fingerprints of seizure discharges in six patients (five with typical absence seizures, one with complex partial seizures) revealed significant nonlinear interactions. The timing and pattern of these interactions correlated closely with the seizure type. Nonlinear autoregressive (NLAR) analysis is compared with other nonlinear dynamical measures that have been applied to the EEG.
我们描述了一种基于具有非线性反馈特征的模型来表征脑电图（EEG）信号的方法。通过这种方法获得的特征性EEG“指纹”显示了非线性相互作用的时间进程，而非标准频谱分析易检测到的方面。对6例患者（5例典型失神发作，1例复杂部分性发作）的癫痫放电指纹分析显示出显著的非线性相互作用。这些相互作用的时间和模式与癫痫发作类型密切相关。将非线性自回归（NLAR）分析与其他已应用于EEG的非线性动力学测量方法进行了比较。
