Epileptic seizures as extreme events in the human brain: Possibilities for prediction and prevention

癫痫影响着全世界6000万人，约占世界人口的1%。在约25%的癫痫患者中，癫痫发作无法通过任何可用的治疗方法控制。一种能够检测癫痫发作前兆的方法可以显著提高癫痫患者的治疗可能性，从而提高患者的生活质量。然而，目前用于从正在进行的脑电(EEG)中检测癫痫前体的分析技术还不足以提供临床满意的解决方案。因此，在上一个筹资期间，全世界都作出了努力，以改善这种情况。使用我们的脑电分析和神经网络建模方法，我们可以更好地描述癫痫脑网络中癫痫发作产生过程的时空特征。然而，我们也发现了一些鲜为人知的因素，这些因素似乎影响了分析和建模方法，从而阻碍了对癫痫前体的更好检测和对癫痫过程的更好理解。因此，为了实现该项目的长期目标--开发癫痫发作预测的自动化系统--我们的目标是进一步改进我们的分析和建模方法，并通过这两种方法的结合来加强基于脑电的癫痫发作预测。

Epilepsy affects 60 million humans worldwide, approximately 1% of the world population. In about 25% of individuals with epilepsy, seizures cannot be controlled by any available therapy. A method capable of detecting seizure precursors could significantly improve the therapeutic possibilities and thereby the quality of life for epilepsy patients. Currently available analysis techniques used to detect seizure precursors from the ongoing electroencephalogram (EEG), however, are not yet sufficient to provide clinically satisfactory solutions. During the previous funding period world-wide efforts have thus been made to improve this situation. With our EEG analysis and neuron network modeling approaches we could achieve an improved characterization of spatial-temporal aspects of the seizure generating process in epileptic brain networks. Nevertheless, we also identified a number of poorly understood factors that appear to affect analytical and modeling approaches, thus hampering an improved detection of seizure precursors and an improved understanding of the epileptic process. In order to achieve the long-term goal of this project - the development of an automated system for the prediction of epileptic seizures - we therefore aim to further improve our analytical and modeling approaches, and to enhance the EEG-based prediction of seizures through a combination of both approaches.