title: Brain dynamics based automated epileptic seizure detection.
abstract: 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 (ASTLmax) 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.
authors.forename: V
authors.initials: V
authors.lastname: Venkataraman
authors.forename: I
authors.initials: I
authors.lastname: Vlachos
authors.forename: A
authors.initials: A
authors.lastname: Faith
authors.forename: B
authors.initials: B
authors.lastname: Krishnan
authors.forename: K
authors.initials: K
authors.lastname: Tsakalis
authors.forename: D
authors.initials: D
authors.lastname: Treiman
authors.forename: L
authors.initials: L
authors.lastname: Iasemidis
mesh_terms: D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D012535:Scalp / Q000503:physiopathology; D012680:Sensitivity and Specificity