Predicting state transitions in brain dynamics through spectral difference of phase-space graphs.

Networks are naturally occurring phenomena that are studied across many disciplines. The topological features of a network can provide insight into the dynamics of a system as it evolves, and can be used to predict changes in state. The brain is a complex network whose temporal and spatial behavior can be measured using electroencephalography (EEG). This data can be reconstructed to form a family of graphs that represent the state of the brain over time, and the evolution of these graphs can be used to predict changes in brain states, such as the transition from preictal to ictal in patients with epilepsy. This research proposes objective indications of seizure onset observed from minimally invasive scalp EEG. The approach considers the brain as a complex nonlinear dynamical system whose state can be derived through time-delay embedding of the EEG data and characterized to determine change in brain dynamics related to the preictal state. This method targets phase-space graph spectra as biomarkers for seizure prediction, correlates historical degrees of change in spectra, and makes accurate prediction of seizure onset. A significant trend of normalized dissimilarity over time indicates a departure from the norm, and thus a change in state. Our methods show high sensitivity (90-100%) and specificity (90%) on 241 h of scalp EEG training data, and sensitivity and specificity of 70%-90% on test data. Moreover, the algorithm was capable of processing 12.7 min of data per second on an Intel Core i3 CPU in Matlab, showing that real-time analysis is viable.