Real-Time Monitoring of Epileptic Seizures through Recurrence Time Analysis of EEGs
We propose a recurrence time based approach to characterize brain electrical activity. Unlike many other nonlinear methods, the proposed approach does not require that the EEG data be either chaotic or stationary. It works well on clean signals and is robust to noise. Furthermore, it only contains a few parameters that are largely signal-independent, and hence, is very easy to use. By analyzing single channel as well as multiple channel human EEG data, it is shown that the method is able to accurately detect epileptic seizures and effectively reveal seizure propagation information in the brain. Most critically, the method is very fast - it can real-time on-line process about 100 channel EEG data with a typical PC. Therefore, it has the potential to be an excellent candidate for real-time monitoring of the occurrence and propagation of epileptic seizures in a clinical setting.