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A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction - 2021

A Novel Multi-Scale Dilated 3d Cnn For Epileptic Seizure Prediction

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

Journal name:  

Conferrence name:  2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)

Publisher name:  IEEE

DOI:  10.1109/AICAS51828.2021.9458571

Volume Information: