Awadhesh Prasad

Long-term prospective on-line real-time seizure prediction

OBJECTIVE Epilepsy, one of the most common neurological disorders, constitutes a unique opportunity to study the dynamics of spatiotemporal state transitions in real, complex, nonlinear dynamical systems. In this study, we evaluate the performance of a prospective on-line real-time seizure prediction algorithm in two patients from a common database. METHODS We previously demonstrated that measures of chaos and angular frequency, estimated from electroencephalographic (EEG) signals recorded at critical sites in the cerebral cortex, progressively converge (i.e. become dynamically entrained) as the epileptic brain transits from the asymptomatic interictal state to the ictal state (seizure) (Iasemidis et al., 2001, 2002a, 2003a). This observation suggested the possibility of developing algorithms to predict seizures well ahead of their occurrences. One of the central points in those investigations was the application of optimization theory, specifically quadratic zero-one programming, for the selection of the critical cortical sites. This current study combines that observation with a dynamical entrainment detection method to prospectively predict epileptic seizures. The algorithm was tested in two patients with long-term (107.54h) and multi-seizure EEG data B and C (Lehnertz and Litt, 2004). RESULTS Analysis from the 2 test patients resulted in the prediction of up to 91.3% of the impending 23 seizures, about 89+/-15min prior to seizure onset, with an average false warning rate of one every 8.27h and an allowable prediction horizon of 3h. CONCLUSIONS The algorithm provides warning of impending seizures prospectively and in real time, that is, it constitutes an on-line and real-time seizure prediction scheme. SIGNIFICANCE These results suggest that the proposed seizure prediction algorithm could be used in novel diagnostic and therapeutic applications in epileptic patients.

Complicated basins in external-cavity semiconductor lasers

We demonstrate that complicated basins of attraction can occur in time-delay coupled, external-cavity semiconductor lasers. In particular, we find that there can be multiple coexisting attractors associated with low-frequency fluctuations in the laser power output, and prediction of the asymptotic attractor for specific initial conditions is practically impossible.

Low-frequency fluctuations in external cavity semiconductor lasers: understanding based on a simple dynamical model

We investigate the dynamical origin of low-frequency fluctuations (LFFs) in external cavity semiconductor lasers by utilizing a simplified, three-dimensional model derived from the infinite-dimensional Lang-Kobayashi (LK) equations. The simplified model preserves the dynamical properties of the external-cavity modes (ECMs) and antimodes which play a fundamental role in the generation of LFFs. This model yields a clear picture of the dynamical origin of the LFFs. Two distinct regions are observed in parameter space, one with the presence of the maximum-gain mode (MGM), and another without it. In particular, we show that, in the absence of noise, LFFs are a consequence of the dynamical interactions among different ECMs and antimodes. When a small amount of noise is present, LFFs result from an intermittent switching of trajectories among different coexisting attractors in the phase space. The presence of double peaks in the distribution of power dropout times, which has been observed recently in experiments, is explained, and a scaling relation is obtained between the average switching time and the noise strength.