Hilbert Vibration Decomposition-based epileptic seizure prediction with neural network

Epilepsy is one of the most prominent brain disorders in the world, and epileptic patients suffer from sudden seizures that have a substantial negative impact on their lives. A seizure prediction system, therefore, is essential in overcoming the difficulties that epileptic individuals experience. This study designs and demonstrates a non-patient specific seizure prediction system that uses the Hilbert Vibration Decomposition (HVD) method on surface EEG recordings of 10 patients from the CHB-MIT database. EEG signals with 18 channels are decomposed to 7 subcomponents with the HVD in sliding windows. These subcomponents from all channels are then used to calculate features to be fed into an MLP classifier. The classification process is performed for all patients simultaneously and without relaying information concerning patient identity to the classifier. After the classification stage, an alarm algorithm that evaluates the frequency of preictal predictions is developed. The classification sensitivity was, on average, 19.89% across patients. This sensitivity was increased to, on average 89.8% within 120 min and an average false alarm rate of 0.081/h was achieved with a seizure prediction horizon of 4 min across patients after alarm creation.