A new deep-learning model developed to detect and classify epileptic seizure subjects has higher accuracy and lower computational time than conventional models, new research shows.
– Researchers have developed a convolutional neural network (CNN) model, a type of deep learning model, for classifying epileptic seizures that is designed to provide maximum accuracy and minor computational complexity, according to a study published in Soft Computing.
The researchers developed their algorithm by integrating CNN architecture with a hierarchical attention mechanism, which was expected to enhance the model’s performance. The model comprises three parts: a feature extraction layer, a hierarchical attention layer, and a classification layer.
The model, which also uses a support vector machine (SVM) algorithm, analyzes a feature map obtained from the raw EEG signal and determines whether the EEGs it was taken from are “healthy” or “seizure.”