IEEE Trans Biomed Eng. 2018 Sep;65(9):2109-2118. doi: 10.1109/TBME.2017.2785401. Epub 2017 Dec 25.
This paper investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish between the interictal and preictal regions. The second aim is to define a prediction horizon in which the prediction is as accurate and as early as possible, clearly two competing objectives.
Convolutional filters on the wavelet transformation of the EEG signal are used to define and learn quantitative signatures for each period: interictal, preictal, and ictal. The optimal seizure prediction horizon is also learned from the data as opposed to making an a priori assumption.
Computational solutions to the optimization problem indicate a 10-min seizure prediction horizon. This result is verified by measuring Kullback-Leibler divergence on the distributions of the automatically extracted features.
The results on the EEG database of 204 recordings demonstrate that (i) the preictal phase transition occurs approximately ten minutes before seizure onset, and (ii) the prediction results on the test set are promising, with a sensitivity of 87.8% and a low false prediction rate of 0.142 FP/h. Our results significantly outperform a random predictor and other seizure prediction algorithms.
We demonstrate that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures.
本文研究了使用头皮脑电图（EEG）数据预测局灶性癫痫发作的假设。我们的第一个目标是学习区分发作间期和发作前期区域的特征。第二个目标是定义一个预测窗口，在该窗口中，预测尽可能准确和尽早，这显然是两个相互竞争的目标。
使用 EEG 信号的小波变换上的卷积滤波器来定义和学习每个时期的定量特征：发作间期、发作前期和发作期。还从数据中学习最佳的癫痫发作预测窗口，而不是进行先验假设。
优化问题的计算解决方案表明，癫痫发作的预测窗口为 10 分钟。通过测量自动提取特征的分布的 Kullback-Leibler 散度，验证了这一结果。
对 204 个记录的 EEG 数据库的结果表明，（i）发作前期的相变大约在癫痫发作前 10 分钟发生，（ii）测试集上的预测结果很有希望，灵敏度为 87.8%，假阳性预测率低至 0.142 FP/h。我们的结果明显优于随机预测器和其他癫痫发作预测算法。
我们证明可以从头皮 EEG 中学习到一组稳健的特征，这些特征可以描述局灶性癫痫发作的发作前期状态。
