Lehnertz Klaus
Department of Epileptology, and Helmholtz-Institute for Radiation and Nuclear Physics, and Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany.
J Biol Phys. 2008 Aug;34(3-4):253-66. doi: 10.1007/s10867-008-9090-3. Epub 2008 Jul 9.
This overview summarizes findings obtained from analyzing electroencephalographic (EEG) recordings from epilepsy patients with methods from the theory of nonlinear dynamical systems. The last two decades have shown that nonlinear time series analysis techniques allow an improved characterization of epileptic brain states and help to gain deeper insights into the spatial and temporal dynamics of the epileptic process. Nonlinear EEG analyses can help to improve the evaluation of patients prior to neurosurgery, and with an unequivocal identification of precursors of seizures, they can be of great value in the development of seizure warning and prevention techniques.
本综述总结了运用非线性动力系统理论方法分析癫痫患者脑电图(EEG)记录所获得的研究结果。过去二十年表明,非线性时间序列分析技术能够更好地表征癫痫脑状态,并有助于更深入地洞察癫痫发作过程的时空动态。非线性脑电图分析有助于改善神经外科手术前患者的评估,并且通过明确识别癫痫发作的先兆,其在癫痫发作预警和预防技术的开发中具有重要价值。