Quian Quiroga R, Kreuz T, Grassberger P
John von Neumann Institute for Computing, Forschungszentrum Jülich, D-52425 Jülich, Germany.
Phys Rev E Stat Nonlin Soft Matter Phys. 2002 Oct;66(4 Pt 1):041904. doi: 10.1103/PhysRevE.66.041904. Epub 2002 Oct 15.
We propose a simple method to measure synchronization and time-delay patterns between signals. It is based on the relative timings of events in the time series, defined, e.g., as local maxima. The degree of synchronization is obtained from the number of quasisimultaneous appearances of events, and the delay is calculated from the precedence of events in one signal with respect to the other. Moreover, we can easily visualize the time evolution of the delay and synchronization level with an excellent resolution. We apply the algorithm to short rat electroencephalogram (EEG) signals, some of them containing spikes. We also apply it to an intracranial human EEG recording containing an epileptic seizure, and we propose that the method might be useful for the detection of epileptic foci. It can be easily extended to other types of data and it is very simple and fast, thus being suitable for on-line implementations.
我们提出了一种简单的方法来测量信号之间的同步和时延模式。它基于时间序列中事件的相对时间，例如定义为局部最大值。同步程度由事件准同时出现的次数获得，时延则根据一个信号中事件相对于另一个信号的先后顺序来计算。此外，我们能够以极高的分辨率轻松可视化时延和同步水平的时间演变。我们将该算法应用于短的大鼠脑电图（EEG）信号，其中一些包含尖峰。我们还将其应用于包含癫痫发作的颅内人类EEG记录，并提出该方法可能对癫痫病灶的检测有用。它可以很容易地扩展到其他类型的数据，而且非常简单快速，因此适合在线实现。
