How well can epileptic seizures be predicted? An evaluation of a nonlinear method. Academic Article uri icon

Overview

abstract

  • The unpredictability of the occurrence of epileptic seizures contributes to the burden of the disease to a major degree. Thus, various methods have been proposed to predict the onset of seizures based on EEG recordings. A nonlinear feature motivated by the correlation dimension is a seemingly promising approach. In a previous study this method was reported to identify 'preictal dimension drops' up to 19 min before seizure onset, exceeding the variability of interictal data sets of 30-50 min duration. Here we have investigated the sensitivity and specificity of this method based on invasive long-term recordings from 21 patients with medically intractable partial epilepsies, who underwent invasive pre-surgical monitoring. The evaluation of interictal 24-h recordings comprising the sleep-wake cycle showed that only one out of 88 seizures was preceded by a significant preictal dimension drop. In a second analysis, the relation between dimension drops within time windows of up to 50 min before seizure onset and interictal periods was investigated. For false-prediction rates below 0.1/h, the sensitivity ranged from 8.3 to 38.3% depending on the prediction window length. Overall, the mean length and amplitude of dimension drops showed no significant differences between interictal and preictal data sets.

publication date

  • September 23, 2003

Research

keywords

  • Epilepsies, Partial
  • Nonlinear Dynamics
  • Signal Processing, Computer-Assisted

Identity

Scopus Document Identifier

  • 0345306183

Digital Object Identifier (DOI)

  • 10.1093/brain/awg265

PubMed ID

  • 14506067

Additional Document Info

volume

  • 126

issue

  • Pt 12