Fast monitoring of epileptic seizures using recurrence time statistics of electroencephalography - Gao, Jianbo; Hu, Jing (Frontiers in Computational Neuroscience, 2013)
Epilepsy is one of the most common disorders of the brain. Currently, determination of epileptic seizures often involves tedious, time-consuming visual inspection of electroencephalography (EEG) data by medical experts. To better monitor seizures and make medications more effective, we propose a recurrence time based approach to characterize brain electrical activity. Recurrence times have a number of distinguished properties that make it very effective for forwarning epileptic seizures as well as studying propagation of seizures: 1) recurrence times amount to periods of periodic signals, 2) recurrence times are closely related to information dimension, Lyapunov exponent, and Kolmogorov entropy of chaotic signals, 3) recurrence times embody Shannon and Renyi entropies of random fields, and 4) recurrence times can readily detect bifurcation-like transitions in dynamical systems. In particular, property 4) dictates that unlike many other nonlinear methods, recurrence time method does not require the EEG data be chaotic and/or stationary. Moreover, the method only contains a few parameters that are largely signal-independent, and hence, is very easy to use. The method is also very fast—it is fast enough to on-line process multi-channel EEG data with a typical PC. Therefore, it has the potential to be an excellent candidate for real-time monitoring of epileptic seizures in a clinical setting.