Event synchronization: A simple and fast method to measure synchronicity and time delay patterns

TLDR

The authors propose a simple, fast method to measure synchronization and time‑delay patterns between signals and suggest its use for detecting epileptic foci. The method quantifies synchronization by counting quasi‑simultaneous events defined as local maxima, computes delay from event precedence, and can be visualized over time, as demonstrated on rat and human EEG data.

Abstract

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.