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.