A Deep Learning-Based Method for Automatic Detection of Epileptic Seizure in a Dataset With Both Generalized and Focal Seizure Types

Epilepsy is the second most popular neurological disorder affecting 65 million people around the world. Seizures are classified into two kinds; focal and generalized ictal activities, reflecting the spread of seizure activity on the brain. Focal seizures start and affect specific regions of the brain, whereas the generalized propagates throughout the brain. Current approaches to developing an automatic seizure detection algorithm do not consider the types of seizures. However, to detect the focal seizures, the locations of onset of seizure must be identified by an expert through inspection of the electroencephalogram (EEG), which is an expensive and time-consuming procedure. Moreover, most proposed methods are patient-specific and cannot be generalized on an unseen patient, limiting the clinical usage of previous studies. This work presents a generalizable seizure detection algorithm by considering different seizure types. After pre-processing data and rejecting artifacts, a deep neural network is used to extract robust representations across seizures and a population. The proposed method includes deep recurrent and convolutional neural networks to capture spatial and temporal information simultaneously. Experiments on the TUH EEG seizure dataset, which contains both generalized and focal seizures, show that the proposed method increases the accuracy over state-of-the-art from 80.72% to 82%, precision from 67.55% to 71.69%, and sensitivity from 80% to 85%.