Seizure prediction and control in epilepsy

pg 1 LIST OF ABBREVIATIONS IN ALPHABETIC ORDER pg 2 INTRODUCTION 1. Historical background. pg 3 1.1 Epileptic seizures. pg 4 2. Epilepsy and electroencephalography. pg 7 2.1 Spatial-temporal dynamics in epilepsy. pg 8 • PREDICTION 3. Detection and prediction of seizures on scalp-EEG data. pg 9 3.1 Linear and non linear measures to predicting of seizures. pg 10 • CONTROL 4. Seizures control. pg 15 4.1 Slow cortical potentials (SCPs). pg 16 5. Neurofeedback for seizure control. pg 17 6. Mental simulation: a possible link between epilepsy and schizophrenia. pg 20 MATERIALS AND METHODS • PREDICTION 7. Implementation of Permutation Entropy (PE) on scalp-EEG data. pg 23 7.1 Statistical evaluation of PE for predictability of seizures. pg 28 7.2 Mental imagery task. pg 29 7.3 The longitudinal study: predicting epileptic seizures by a mental imagery task. pg 31 • CONTROL 8. Control of epileptic seizures by means of Neurofeedback: the experimental set up. pg 38 RESULTS • PREDICTION 9. PE as suitable measure for detection of changes in vigilance states. pg 40 9.1 Mental simulation task for seizure prediction. pg 41 • CONTROL 9.2 Modulation of slow cortical potentials in epileptic patients. pg 48 DISCUSSION AND CONCLUSIONS • PREDICTION 10 Better prediction could mean better control over epileptic seizures. pg 51 10.1 A common behavioural side of paranoid schizophrenia and temporal lobe epilepsy: pg 53 Could motor imagery be a key for seizures prediction? • CONTROL 10.2 Seizure reduction by SCPs Neurofeeback. pg 54 10.3 Running the future: full self-management of seizures. pg 55 10.4 Summary and outlook. pg 56 REFERENCES pg 57 APPENDIX ABIOGRAPHICAL SKETCH. pg 69 BLIST OF PUBLICATIONS. pg 70 CPOSTERS. pg 73