Brain dynamics based automated epileptic seizure detection

We developed and tested a seizure detection algorithm based on two measures of nonlinear and linear dynamics, that is, the adaptive short-term maximum Lyapunov exponent (ASTL_max) and the adaptive Teager energy (ATE). The algorithm was tested on long-term (0.5-11.7 days) continuous EEG recordings from five patients (3 with intracranial and 2 with scalp EEG) with a total of 56 seizures, producing a mean sensitivity of 91% and mean specificity of 0.14 false positives per hour. The developed seizure detection algorithm is data-adaptive, training-free, and patient-independent.