title: Pharmaco-resistant seizures: self-triggering capacity, scale-free properties and predictability?
abstract: Relevant and timely questions such as regarding the predictability of seizures and their capacity to trigger more seizures remain the subject of debate in epileptology. The present study endeavors to gain insight into these dynamic issues by adopting a non-reductionist approach and via the use of mathematical tools. Probability distribution functions of seizure energies and inter-seizure intervals and the probability of seizure occurrence conditional upon the time elapsed from the previous seizure were estimated from prolonged recordings from subjects with pharmaco-resistant seizures, undergoing surgical evaluation, on reduced doses of or on no medications. The energy and inter-seizure interval distributions for pharmaco-resistant seizures, under the prevailing study conditions, are governed by power laws ('scale-free' behavior). Pharmaco-resistant seizures tend to occur in clusters and the time to the next seizure increases with the duration of the seizure-free interval since the last one. However, characteristic size energy probability density functions were found in a few subjects. These findings suggests that: (i) pharmaco-resistant seizures have an inherent self-triggering capacity; (ii) their time of occurrence and intensity may be predictable in light of the existence of power law distributions and of their self-triggering capacity; and (iii) their lack of typical size and duration (scale-free), features upon which their classification into ictal or interictal is largely based, may be inadequate/insufficient classifiers.
authors.affiliation: Department of Neurology, The University of Kansas Medical Center, Kansas City, KS, USA. iosorio@kumc.edu
authors.forename: Ivan
authors.initials: I
authors.lastname: Osorio
authors.forename: Mark G
authors.initials: MG
authors.lastname: Frei
authors.forename: Didier
authors.initials: D
authors.lastname: Sornette
authors.forename: John
authors.initials: J
authors.lastname: Milton
mesh_terms: D016000:Cluster Analysis; D004569:Electroencephalography; D006801:Humans; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D011336:Probability; D012640:Seizures / Q000503:physiopathology* / Q000523:psychology*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors