论文标题
多通道合成前脑电图信号以增强癫痫发作的预测
Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures
论文作者
论文摘要
癫痫是一种慢性神经系统疾病，影响了全球1 \％的深度学习（DL）算法的脑电图（EEG）分析（EEG）分析为准确的癫痫发作（ES）预测提供了可能性，从而使患者受益于患有癫痫患者。为了确定癫痫发作开始前的前区域，需要大量带注释的脑电图信号才能训练DL算法。但是，癫痫发作的稀缺性导致培训DL算法的数据不足。为了克服这些数据不足，在本文中，我们提出了一种基于生成对抗网络的人工信号综合算法，以生成合成的多通道EEG EEG EEG PERICTAL样品。由视觉和统计评估确定的高质量单通道体系结构用于训练多通道样本的发电机。通过比较没有合成前样品增强的ES预测性能来评估合成样品的有效性。在接收器操作特征曲线评估下，保留的一对交叉验证ES预测准确性和相应的面积从73.0 \％和0.676和0.676和0.704提高到10 $ \ times $ synthetic样品增强。获得的结果表明，合成前样品可有效增强ES预测性能。
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.
