Detection of Onset in Epilepsy Signals using Generalized Gaussian Distribution

ANTONIO QUINTERO RINCÓN; HADJ BATATIA; MARCELO PEREYRA; MARCELO RISK

Cordoba

Conferencia; Fifth International Conference on Advances in New Technologies, Interactive Interfaces and Communicability; 2014

Association for Computing Machinery (ACM) and ALAIPO

Extracting information from scalp EEG signals is a challenging biomedical signal processing problem that has a potentially strong impact in the diagnosis and treatment of numerous neurological conditions. In this work westudy a new methodology for extracting information from EEG signals from patients suffering from epilepsy. The methodology is based on a multiresolution wavelet representation and a statistical generalized Gaussian model,which provide a compact description of the time-frequency information in the EEG signal array. Preliminary experiments suggest that the information captured by the model is potentially useful for effectively detecting the onset of epileptic seizures, which is key for epilepsy diagnosis and treatment.