title: Detection of seizure rhythmicity by recurrences.
abstract: Epileptic seizures show a certain degree of rhythmicity, a feature of heuristic and practical interest. In this paper, we introduce a simple model of this type of behavior, and suggest a measure for detecting and quantifying it. To evaluate our method, we develop a set of test segments that incorporate rhythmicity features, and present results from the application of this measure to test segments. We then analyze electrocorticogram segments containing seizures, and present two examples. Finally, we discuss the similarity of our method to techniques for detecting unstable periodic orbits in chaotic time series.
authors.affiliation: WVHTC Foundation, 1000 Technology Drive, Suite 1000, Fairmont, West Virginia 26554, USA. mharrison@wvhtf.org
authors.forename: Mary Ann F
authors.initials: MA
authors.lastname: Harrison
authors.forename: Mark G
authors.initials: MG
authors.lastname: Frei
authors.forename: Ivan
authors.initials: I
authors.lastname: Osorio
mesh_terms: D000465:Algorithms; D001683:Biological Clocks; D001921:Brain / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D017711:Nonlinear Dynamics; D009991:Oscillometry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010507:Periodicity