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

TL;DR

This paper introduces a novel multi-scale dilated 3D CNN that effectively analyzes EEG signals for epileptic seizure prediction, achieving superior accuracy, sensitivity, and specificity over existing methods.

Contribution

The paper presents a new 3D CNN with multiscale dilated convolution for improved EEG feature extraction in seizure prediction.

Findings

Achieves 80.5% accuracy on CHB-MIT EEG dataset.

Outperforms existing state-of-the-art methods.

Demonstrates high sensitivity and specificity.

Abstract

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 multiscale 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.