Abstract
Epilepsy is a relatively common brain disorder which may be very debilitating. 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 forewarning 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 non-linear 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.
Highlights
Epilepsy is a relatively common brain disorder which may be very debilitating
By comparing seizure detection using a variety of complexity measures from deterministic chaos theory, random fractal theory, and information theory, we have found that the variations of those complexity measures with time have two patterns—either similar or reciprocal (Gao et al, 2011a)
While we leave the details to our prior works (Gao et al, 2006a, 2007, 2012a,b), these results suggest that it would be sufficient for us to compare the performance of the recurrence time based method for seizure detection with the performance of any of the existing complexity measures
Summary
Epilepsy is a relatively common brain disorder which may be very debilitating. It affects approximately 1% of the world population (Jallon, 1997) and three million people in the United States alone. The normal activity of the central nervous system is disrupted. Clinical effects may include motor, sensory, affective, cognitive, automatic and physical symptomatology. Epilepsy can be treated effectively in many instances, severe side effects may result from constant medication. To make medications more effective, timely detection of seizure is very important
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