A general framework of a system for early seizure detection and alert is presented. Many studies have shown high potential of electroencephalograms (EEG) when there are used together with machine learning algorithms for seizure/non-seizure classification task. In this paper, mainly guidelines will be presented on how to use convolutional neural networks for the purpose of highly accurate classification of non-invasive EEG for patients with epilepsy. Convolutional neural networks can be pre-trained on a sample data as described in this paper and then implemented into an application or a device, which readjusts its parameters according to the patient-specific EEG patterns and thus can be further used as a seizure monitoring and alert system. The paper also demonstrated how transfer learning can be applied to create a patient-specific classifier with high accuracy.
