查看文章

Epilepsy is a common neurological disease that uses electroencephalogram (EEG) data for its detection purpose. Neurologists make the diagnosis by visual inspection of EEG reports. As it is time-consuming and due to the shortage of specialists worldwide, researchers have proposed automated systems to detect the disease. In the past decade, most of the systems were designed using hand-engineered features. However, identifying appropriate features is always a challenging task in the development of a seizure detector system. Deep learning networks eliminate the problem of selecting the best features but suffer from long training time, generally days or weeks. To overcome this problem, the authors have proposed a new 1D convolutional neural network (CNN) that automatically extracts features at an average of seven epochs, only followed by traditional machine learning (ML) classifier. 1D CNN architectures …