Automated pre-ictal phase detection algorithm from EEG signals

Epilepsy poses a significant burden to society due to associated healthcare costs to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologists diagnose and determine the patient´s treatment. Electroencephalogram has been traditionally used to diagnose patients by evaluating brain functions that might correspond to epilepsy. This research focuses on developing a new classification technique and the prediction of pre-ictal states that announce epileptic seizures, from the direct EEG data analysis. The approach involves the placement of electrodes on critical regions on the patient´s head as a part of a telemetry system which communicates with the EEG recorder and the DSP unit that performs automated pre-ictal state detection based on the obtained EEG signal. Studying potential preconditions and alerting the patient about a possible seizure attack so that s/he can take safety precautions has a potential to improve patient care management.