Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures

TL;DR

This paper introduces a generative adversarial network-based method to synthesize multichannel preictal EEG signals, significantly improving epileptic seizure prediction accuracy by augmenting training data.

Contribution

It proposes a novel synthetic EEG signal generation technique to address data scarcity in deep learning-based seizure prediction models.

Findings

Synthetic samples improved prediction accuracy from 73% to 78%.

Area under the ROC curve increased from 0.676 to 0.704.

10× data augmentation enhanced seizure prediction performance.

Abstract

Epilepsy is a chronic neurological disorder affecting 1\% of people worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for accurate epileptic seizure (ES) prediction, thereby benefiting patients suffering from epilepsy. To identify the preictal region that precedes the onset of seizure, a large number of annotated EEG signals are required to train DL algorithms. However, the scarcity of seizure onsets leads to significant insufficiency of data for training the DL algorithms. To overcome this data insufficiency, in this paper, we propose a preictal artificial signal synthesis algorithm based on a generative adversarial network to generate synthetic multichannel EEG preictal samples. A high-quality single-channel architecture, determined by visual and statistical evaluations, is used to train the generators of multichannel samples. The effectiveness of the synthetic samples is evaluated by comparing the ES prediction performances without and with synthetic preictal sample augmentation. The leave-one-seizure-out cross validation ES prediction accuracy and corresponding area under the receiver operating characteristic curve evaluation improve from 73.0\% and 0.676 to 78.0\% and 0.704 by 10$\times$ synthetic sample augmentation, respectively. The obtained results indicate that synthetic preictal samples are effective for enhancing ES prediction performance.