Gotman J
Electroencephalogr Clin Neurophysiol Suppl. 1985;37:133-45.
The processing of EEGs with respect to epileptic seizures falls in two main categories: automatic recognition of seizures during long-term monitoring and analysis of seizures to extract information not available from visual inspection. Automatic seizure recognition is a complex problem because the EEG during seizures is not well defined morphologically; imperfect automatic recognition is nevertheless possible and it can simplify the task of monitoring and increase its diagnostic yield. The widespread use of monitoring techniques has made the recording of seizures relatively frequent. It has therefore become worthwhile to develop procedures for analyzing seizure patterns in detail, particularly the propagation of seizure activity between different brain structures. The computation of time differences of a few milliseconds between two channels is possible and has been shown to be meaningful. Such an analysis allows to follow the structures involved and the different stages of evolution of a seizure, the structures likely to be 'driving' seizure activity and the possible routes of propagation. These methods are reviewed and their use is illustrated.
长期监测期间癫痫发作的自动识别以及癫痫发作分析以提取肉眼检查无法获得的信息。自动癫痫发作识别是一个复杂的问题，因为癫痫发作期间的脑电图在形态上没有明确的定义；然而，不完全的自动识别是可能的，并且它可以简化监测任务并提高其诊断率。监测技术的广泛使用使得癫痫发作的记录相对频繁。因此，开发详细分析癫痫发作模式的程序变得很有价值，特别是癫痫活动在不同脑结构之间的传播。计算两个通道之间几毫秒的时间差是可能的，并且已被证明是有意义的。这样的分析可以追踪涉及的结构以及癫痫发作演变的不同阶段、可能“驱动”癫痫活动的结构以及可能的传播途径。对这些方法进行了综述并举例说明了它们的用途。
