Details of the dataset are described in a later section. The Bonn University EEG database is widely used, which is publicly available and labeled as A, B, C, D, and E. Thereinto, most of the traditional methods use hand-engineered techniques for feature extraction from EEG signals and then conjunct with classifiers to recognize. These methods can be roughly classified into two categories: conventional methods and deep learning- (DL-) based methods. Numerous algorithms have been proposed in the literature for automatic detection of epileptic seizures. Therefore, scientific research on EEG-based automatic detection of epilepsy has attracted much attention. The visual inspection of EEG for seizure detection by expert neurologists is a time-consuming and laborious process, and the diagnosis may not be accurate because of the massive amounts of EEG data and the discrepant clinical judgment standards of different neurologists.
Seizures are transient neurological dysfunctions caused by abnormal brain neurons and excessive supersynchronized discharges. EEG is one of the critical technologies to identify an abnormality of the brain, such as detecting epileptic seizures. IntroductionĮlectroencephalogram (EEG) is a noninvasive, effective technique used in clinical studies to decode the electrical activity of the brain. Model performance is evaluated on the University of Bonn dataset, which achieves the accuracy of 97.63%∼99.52% in the two-class classification problem, 96.73%∼98.06% in the three-class EEG classification problem, and 93.55% in classifying the complicated five-class problem. Thereinto, each convolutional block consists of five types of layers: convolutional layer, batch normalization layer, nonlinear activation layer, dropout layer, and max-pooling layer. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which composes of three convolutional blocks and three fully connected layers. Traditional EEG recognition models largely depend on artificial experience and are of weak generalization ability.
Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures.