title: The impact of time on seizure prediction performance in the FSPEEG database.
abstract: We demonstrate evidence that high discriminability between preictal and interictal intracranial electroencephalogram (iEEG) recordings [1,2] of the Freiburg database (FSPEEG) may be due to the amount of time that occurred between recordings, as opposed to the underlying seizure state, i.e., preictal or interictal. After replicating published classification methods and results, we performed two experiments. In the first experiment, almost perfect discriminability between discontinuous interictal recordings and almost perfect discriminability between discontinuous preictal recordings were observed as the amount of time between recordings increased. Further, a second experiment demonstrated that the classification performance for patients with large time gaps between preictal and interictal recordings was noticeably higher than the classification performance for patients with contiguous preictal and interictal files. These results provide evidence that time likely plays a major role in the discriminability of the iEEG features considered in this study, regardless of the underlying seizure state. Feature nonstationarity is present and may, under certain conditions, lead to overestimation or underestimation of the probability of seizure occurrence.
authors.affiliation: Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Czarnek
authors.affiliation: Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Morton
authors.affiliation: Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA. Electronic address: leslie.collins@duke.edu.
authors.forename: Leslie
authors.initials: L
authors.lastname: Collins
authors.affiliation: Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
authors.forename: Stacy
authors.initials: S
authors.lastname: Tantum
authors.affiliation: Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
authors.forename: Chandra
authors.initials: C
authors.lastname: Throckmorton
mesh_terms: D001921:Brain / Q000503:physiopathology; D016208:Databases, Factual; D000069280:Electrocorticography / Q000379:methods*; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D008297:Male; D008991:Monitoring, Physiologic / Q000379:methods; D011237:Predictive Value of Tests; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D013997:Time Factors
keywords: Detection; Electrocorticography; Electroencephalogram; Epilepsy; FSPEEG; Freiburg; Prediction; SVM; Support vector machine