title: Automated seizure detection: unrecognized challenges, unexpected insights.
abstract: One of epileptology's fundamental aims is the formulation of a universal, internally consistent seizure definition. To assess this aim's feasibility three signal analysis methods were applied to a seizure time series and performance comparisons were undertaken among them and with respect to a validated algorithm. One of the methods uses a Fisher's matrix weighted measure of the rate of parameters change of a 2nd order auto-regressive model, another is based on the Wavelet Transform Maximum Modulus for quantification of changes in the logarithm of the standard deviation of ECoG power and yet another employs the ratio of short-to-long term averages computed from cortical signals. The central finding, fluctuating concordance among all methods' output as a function of seizure duration, uncovers unexpected hurdles in the path to a universal definition, while furnishing relevant knowledge in the dynamical (spectral non-stationarity/varying ictal signal complexity) and clinical (potential un-attainability of consensus) domains. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
authors.affiliation: Comprehensive Epilepsy Center, University of Kansas Medical Center, Kansas City, KS, USA. iosorio@kumc.edu
authors.forename: Ivan
authors.initials: I
authors.lastname: Osorio
authors.forename: Alexey
authors.initials: A
authors.lastname: Lyubushin
authors.forename: Didier
authors.initials: D
authors.lastname: Sornette
mesh_terms: D000465:Algorithms; D058256:Brain Waves / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D012044:Regression Analysis; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors