Research Area:  Machine Learning
Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries. In this work, a novel convolutional neural network (CNN) is proposed to analyze time, frequency, and channel information of electroencephalography (EEG) signals. The model uses three-dimensional (3D) kernels to facilitate the feature extraction over the three dimensions. The application of multi-scale dilated convolution enables the 3D kernel to have more flexible receptive fields. The proposed CNN model is evaluated with the CHB-MIT EEG database, the experimental results indicate that our model outperforms the existing state-of-the-art, achieves 80.5% accuracy, 85.8% sensitivity and 75.1% specificity.
Author(s) Name:  Ziyu Wang; Jie Yang; Mohamad Sawan
Conferrence name:  2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Publisher name:  IEEE
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9458571