An End-to-End Deep Learning Approach for Epileptic Seizure Prediction

TL;DR

This paper introduces an end-to-end deep learning CNN model for epileptic seizure prediction, outperforming traditional feature-based methods on EEG datasets with high accuracy and low false prediction rates.

Contribution

The paper presents a novel CNN architecture for seizure prediction that eliminates the need for handcrafted features, achieving superior performance on EEG datasets.

Findings

Sensitivity of 93.5% and 98.8% on two datasets.

False prediction rate of 0.063/h and 0.074/h.

Area under ROC curve of 0.981 and 0.988.

Abstract

An accurate seizure prediction system enables early warnings before seizure onset of epileptic patients. It is extremely important for drug-refractory patients. Conventional seizure prediction works usually rely on features extracted from Electroencephalography (EEG) recordings and classification algorithms such as regression or support vector machine (SVM) to locate the short time before seizure onset. However, such methods cannot achieve high-accuracy prediction due to information loss of the hand-crafted features and the limited classification ability of regression and SVM algorithms. We propose an end-to-end deep learning solution using a convolutional neural network (CNN) in this paper. One and two dimensional kernels are adopted in the early- and late-stage convolution and max-pooling layers, respectively. The proposed CNN model is evaluated on Kaggle intracranial and CHB-MIT scalp EEG datasets. Overall sensitivity, false prediction rate, and area under receiver operating characteristic curve reaches 93.5%, 0.063/h, 0.981 and 98.8%, 0.074/h, 0.988 on two datasets respectively. Comparison with state-of-the-art works indicates that the proposed model achieves exceeding prediction performance.