Seizure prediction using Hilbert Huang Transform on field programmable gate array

The Hilbert Huang Transform (HHT) has been used extensively in the time-frequency analysis of electroencephalography (EEG) signals and Brain-Computer Interfaces. Most studies utilizing the HHT for extracting features in seizure prediction have used intracranial EEG recordings. Invasive implants in the cortex have unknown long term consequences and pose the risk of complications during surgery. This added risk dimension makes them unsuitable for continuous monitoring as would be the requirement in a Body Area Network. We present an HHT-based system on Field Programmable Gate Array (FPGA) for predicting epileptic seizures using scalp EEG. We use bandwidth features of Intrinsic Mode Functions and obtain a classification accuracy of close to 100% using patient-specific classifiers in software. Details of FPGA implementation are also given.