Zorgno, IvannaBlanc, María CeciliaOxenford, SimónGil Garbagnoli, FranciscoD'Giano, CarlosQuintero-Rincón, Antonio2019-05-292019-05-292018http://ri.itba.edu.ar/handle/123456789/1604"Epilepsy is a disease caused by an excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an ongoing challenge in biomedical signal processing. In this paper, a new method is proposed for onset seizure detection in epileptic EEG signals based on parameters from the t-location-scale distribution coupled with the variance and the Pearson correlation coefficient. The 1-nearest neighbor classifier achieved a 91% sensitivity (True positive rate) and 95% specificity (True Negative Rate) with a delay of 4.5 seconds (on average) in the 45 signals analyzed, which suggests that the proposed methodology is potentially useful for seizure onset detection in epileptic EEG signals."enEPILEPSIAELECTROENCEFALOGRAFIAPROCESAMIENTO DE SEÑALES DIGITALESPóster