Analysis of variations of correlation dimension and nonlinear interdependence for the prediction of pediatric myoclonic seizures - A preliminary study

- Correlation dimension and nonlinear interdependence can predict myoclonic seizures.
- Direction and timing of preictal variations are variable among individual patients.
- Proper selection of channels is pivotal in detecting preictal state.
- Patient-wise tuning of automated predictive system based on scalp EEG is suggested.

In this preliminary study, we evaluated the predictive ability of Correlation Dimension (CD) and Nonlinear Interdependence (NI) for seizures in pediatric myoclonic epilepsy patients. Scalp EEG recordings of eight diagnosed cases of myoclonic epilepsy were analyzed using Receiver Operating Curve (ROC) for discriminating the preictal period from interictal period. Furthermore, based on clinical seizure characteristics and EEG data, the spatiotemporal patterns of measures in clinically relevant areas of the brain were compared with other areas for each patient. CD showed a dominant increasing behavior in both all of the individual channels and channels of clinical interest for 75% of patients. For NI, the dominant direction was also increasing in 62.5% of patients for all of the individual channels and in 75% of patients for channels of clinical interest. However, there was no consistent general behavior in the timing of the preictal change amongst patients and within individual patient. Nonlinear measures of CD and NI can differentiate the preictal phase from the corresponding interictal phase. However, due to high variability, patient-wise tuning of possible automated systems for seizure prediction is suggested. This is the first study to employ nonlinear analysis for seizure prediction in pediatric myoclonic epilepsy.