Seizures had been considered unpredictable and sudden events until a few years ago. The Scientific Community began being interested in epileptic seizure prediction during'70s: some results in literature showed that seizures were likely to be stage of an epileptogenic process rather than an unpredictable and sudden event. Therefore a new hypothesis was proposed: the evolution of brain dynamics towards seizures was assumed to follow this transition: inter-ictal → pre-ictal → ictal → post-ictal.Most of research is focused on intracranial EEG analysis. Recent works from the authors focused on predictability from scalp EEG instead. In particular, they report results about the estimation of the entrainment between critical electrode sites of scalp EEG from epileptic patients: the convergence of STLmax profiles appears detectable also from scalp EEG and this convergence appears linked to the state of the epileptic brain. Moreover, the trend of the convergence allows for the automatic detection of the electrodes involved in the process leading to the seizure. ATSWA (Adaptive Threshold Seizure Warning Algorithm) is an advance seizure warning algorithm that is based on the estimation of STLmax convergence. In order to test ATSWA over scalp EEG, the technique was here implemented and tested over three scalp EEG recordings from patients (A, B, C) affected by partial frontal lobe epilepsy, the average duration was 37 min for patients A and B,whereas the duration for patient C was 5 hours. The technique succeeded in issuing a warning before every seizure,with a warning horizon of 5 min for patient A, 12 for B and of 21.8 min and 101.8 min, for the two seizures of patient C. The technique also automatically selected as critical the electrodes in the focal area. ATSWA seems to be able to detect changes also in the dynamics of scalp EEG as well as to infer information about the critical area, however, further work is required in order to quantify the performance of the technique over long recordings including many seizures.

Epileptic seizure prediction for patients affected by frontal lobe epilepsy

N. MAMMONE;F. C. MORABITO;LA FORESTA, Fabio
2009

Abstract

Seizures had been considered unpredictable and sudden events until a few years ago. The Scientific Community began being interested in epileptic seizure prediction during'70s: some results in literature showed that seizures were likely to be stage of an epileptogenic process rather than an unpredictable and sudden event. Therefore a new hypothesis was proposed: the evolution of brain dynamics towards seizures was assumed to follow this transition: inter-ictal → pre-ictal → ictal → post-ictal.Most of research is focused on intracranial EEG analysis. Recent works from the authors focused on predictability from scalp EEG instead. In particular, they report results about the estimation of the entrainment between critical electrode sites of scalp EEG from epileptic patients: the convergence of STLmax profiles appears detectable also from scalp EEG and this convergence appears linked to the state of the epileptic brain. Moreover, the trend of the convergence allows for the automatic detection of the electrodes involved in the process leading to the seizure. ATSWA (Adaptive Threshold Seizure Warning Algorithm) is an advance seizure warning algorithm that is based on the estimation of STLmax convergence. In order to test ATSWA over scalp EEG, the technique was here implemented and tested over three scalp EEG recordings from patients (A, B, C) affected by partial frontal lobe epilepsy, the average duration was 37 min for patients A and B,whereas the duration for patient C was 5 hours. The technique succeeded in issuing a warning before every seizure,with a warning horizon of 5 min for patient A, 12 for B and of 21.8 min and 101.8 min, for the two seizures of patient C. The technique also automatically selected as critical the electrodes in the focal area. ATSWA seems to be able to detect changes also in the dynamics of scalp EEG as well as to infer information about the critical area, however, further work is required in order to quantify the performance of the technique over long recordings including many seizures.
EEG; Focal seizures; Largest lyapunov exponent
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/211
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