Epileptic seizures prediction based on the combination of EEG and ECG for the application in a wearable device

Epilepsy is a neurological disorder characterized by recurrent and sudden seizures. Recently, researchers found that patients often present physiological abnormalities that precede an epileptic seizure onset. Importantly, these modifications can vary a lot among patients. While the conventional methodology for characterizing epilepsy is electroencephalogram (EEG), some evidences show that electrocardiogram (ECG) can be also useful to assess modifications associated to seizures. In this paper, a preliminary study about the integration of EEG and ECG for a patient-specific seizure prediction is presented. Synchronization patterns from the EEG and time and frequency features, as well as recurrence quantification analysis measures from the inter-beat (RR) series, were extracted. A support vector machine (SVM) classifier was then applied to classify preictal and interictal phases combining features extracted from the two signals. Results showed that, using the proposed combined approach, it is possible to predict the epileptic seizure onset with a total average sensitivity of 93.3%, specificity of 80.6% and a prediction time of about 20 min. This approach could be implemented in portable and wearable devices for a real-life patient-specific seizure prediction.