RATIONALE: One of the most important questions in epileptology is whether specific features can be extracted from time series of brain electrical activity that are predictive of an impending seizure. To this aim, we have investigated and compared the performance of three recently proposed and two established measures, all of them quantifying spatio-temporal variations of interdependence between different areas of the human brain. METHODS: Intracranial multichannel EEG recorded pre-ictally in patients with mesial temporal lobe epilepsy were probed for early indicators of imminent seizure activity. Long-lasting interictal EEG recordings were used to serve as controls. The applied interdependence measures comprised symmetric (cross-correlation C and mutual information M) as well as non-symmetric measures (nonlinear interdependences S and H and transfer entropy T). RESULTS: The measures yielded different degrees of performance in characterizing the temporal variability of interdependences in EEGs recorded shortly before and hours away from an epileptic seizure. Due to their asymmetric character three of the measures provided additional information about the direction of interdependence and were especially suitable to analyze spatio-temporal interactions between the primary epileptogenic area and other parts of the brain. CONCLUSIONS: The analysis of seizure generating patterns using bivariate time series analysis techniques offers a promising approach to the anticipation of epileptic seizures. The new interdependence measures proved useful in gathering additional information about features of brain electrical activity predictive of an impending seizure. This might lead to an deeper insight into mechanisms of ictogenesis and offer new possibilities for therapeutic intervention.
The capability of different interdependence measures to predict epileptic seizures.
T Kreuz;
2001
Abstract
RATIONALE: One of the most important questions in epileptology is whether specific features can be extracted from time series of brain electrical activity that are predictive of an impending seizure. To this aim, we have investigated and compared the performance of three recently proposed and two established measures, all of them quantifying spatio-temporal variations of interdependence between different areas of the human brain. METHODS: Intracranial multichannel EEG recorded pre-ictally in patients with mesial temporal lobe epilepsy were probed for early indicators of imminent seizure activity. Long-lasting interictal EEG recordings were used to serve as controls. The applied interdependence measures comprised symmetric (cross-correlation C and mutual information M) as well as non-symmetric measures (nonlinear interdependences S and H and transfer entropy T). RESULTS: The measures yielded different degrees of performance in characterizing the temporal variability of interdependences in EEGs recorded shortly before and hours away from an epileptic seizure. Due to their asymmetric character three of the measures provided additional information about the direction of interdependence and were especially suitable to analyze spatio-temporal interactions between the primary epileptogenic area and other parts of the brain. CONCLUSIONS: The analysis of seizure generating patterns using bivariate time series analysis techniques offers a promising approach to the anticipation of epileptic seizures. The new interdependence measures proved useful in gathering additional information about features of brain electrical activity predictive of an impending seizure. This might lead to an deeper insight into mechanisms of ictogenesis and offer new possibilities for therapeutic intervention.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
