Schiff N D, Victor J D, Canel A
Department of Neurology and Neuroscience, New York Hospital-Cornell Medical Center, NY 10021, USA.
Biol Cybern. 1995;72(6):527-32. doi: 10.1007/BF00199895.
In a previous study, nonlinear autoregressive (NLAR) models applied to ictal electroencephalogram (EEG) recordings in six patients revealed nonlinear signal interactions that correlated with seizure type and clinical diagnosis. Here we interpret these models from a theoretical viewpoint. Extended models with multiple nonlinear terms are employed to demonstrate the independence of nonlinear dynamical interactions identified in the 'NLAR fingerprint' of patients with 3/s seizure discharges. Analysis of the role of periodicity in the EEG signal reveals that the fingerprints reflect the dynamics not only of the periodic discharge itself, but also of the fluctuations of each cycle about an average waveform. A stability analysis is used to make qualitative inferences concerning the network properties of the ictal generators. Finally, the NLAR fingerprint is analyzed in the context of Volterra-Weiner theory.
在之前的一项研究中，应用于6名患者发作期脑电图（EEG）记录的非线性自回归（NLAR）模型揭示了与癫痫发作类型和临床诊断相关的非线性信号相互作用。在此，我们从理论角度对这些模型进行解读。采用具有多个非线性项的扩展模型来证明在3/s癫痫放电患者的“NLAR指纹”中识别出的非线性动力学相互作用的独立性。对EEG信号中周期性作用的分析表明，这些指纹不仅反映了周期性放电本身的动力学，还反映了每个周期围绕平均波形的波动情况。利用稳定性分析对发作期发生器的网络特性进行定性推断。最后，在Volterra - Wiener理论的背景下对NLAR指纹进行分析。
