ANALYSIS OF THE DYNAMICS OF HUMAN EPILEPTIC SEIZURES FROM SCALP EEG

Purpose: Results in literature show that the convergence of the STLmax (Short Term Maximum Lyapunov Exponent) time series, extracted from intracranial EEG of patients affected by intractable temporal lobe epilepsy, is linked to the seizure onset. 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 based on STLmax convergence. Method: In order to test ATSWA over scalp EEG, the technique was implemented and tested over four scalp EEG recordings: three from three patients (A1, A2 and A3) affected by partial frontal lobe epilepsy and two from a patient affected by absence seizures (patient B), the average duration was 37min for patients A1, A2 and B, whereas the duration for patient A3 was 5hours. Results: The technique succeeded in issuing a warning before every seizure, with a warning horizon of 5min for patient A1, 12 for A2, 4.3min and 7min for the two seizures of patient B, and of 21.8min and 101.8min, for the two seizures of patient A3. The technique automatically selected as critical the electrodes in the focal area, for patient A1, A2 and A3 and in the frontal area, for patient B. Conclusion: ATSWA seems to be able to detect changes in the dynamics of scalp EEG as well as to infer information about the critical area, however, further work is required in order to test the technique over recordings including many seizures