Convolutional Neural Networks for Epileptic Seizure Prediction
This work addresses the need for accurate seizure prediction to reduce uncertainty for epilepsy patients, though it appears incremental as it applies CNNs to a known task without specifying performance gains.
The paper tackled the problem of predicting epileptic seizures by classifying intracranial EEG signals using convolutional neural networks, achieving general applicability on datasets from dogs and patients.
Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.