Hurst parameter estimation for epleptic seizure detection

Abstract

Estimation of the Hurst parameter provides information about the memory range or correlations (long vs. short) of processes (time-series). A new application for the Hurst parameter, real-time event detection, is identified. Hurst estimates using rescaled range, dispersional and bridge- detrended scaled windowed variance analyses of seizure time-series recorded from human subjects reliably detect their onset, termination and intensity. Detection sensitivity is unaltered by signal decimation and window size increases. The high sensitivity to brain state changes, ability to operate in real time and small computational requirements make Hurst parameter estimation using any of these three methods well suited for implementation into miniature implantable devices for contingent delivery of anti-seizure therapies. industrialized world's population, and up to 10% of people in under-developed coun- tries. As seizures are brief and relatively unpredictable, continuous EEG/ECoG moni- toring is needed to implement new therapies, such as contingent electrical stimulation for seizure blockage, via implantable devices, in subjects with pharmaco-resistant epilepsies. Hurst parameter (1) estimation has been applied to many natural (non-biological) (2) and also biological phenomena, such as neuron membrane channel kinetics (10), a fundamental functional operation of the brain. The behavior of membrane channels seems to exhibit long-term correlation (H > 0.78, implying persistence) and the currents recorded through individual ion channels have self-similar properties, that is, they are fractals and may be best modeled using fractional Brownian motion. The fractal behavior may extend to the whole neuron as measured simultaneously across many channels. This raises the possibility that brain electrical processes may be fractal or self-similar, or that, at a minimum, useful information may be obtained from treating them as such in analyzing data or signals generated by these processes in the brain. The Hurst parameter (H) (1, 2) may provide information about the behavior of continuous and discrete event time series and its estimation in the EEG/ECoG of