Patient specific seizure prediction algorithm using Hilbert-Huang Transform

Epilepsy is a neurological disorder that affects about 50 million people around the world. EEG signal processing plays an important role in detection and prediction of epileptic seizures. The aim of this study is to develop a method for early seizure prediction based on Hilbert-Huang Transform. In this patient specific method, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) and first 5 IMFs are used to obtain features for classification of preictal and interictal recordings. Proposed method was tested on Freiburg EEG database. A total of 58 hours of preictal data, prior to 87 seizures, and 490 hours of interictal data were examined. Algorithm resulted in 89.66% sensitivity (78 of 87 seizures) and 0.49 FPs/h using 30 seconds EEG segment with 50% overlap.