IEEE Trans Neural Syst Rehabil Eng. 2020 Dec;28(12):2711-2720. doi: 10.1109/TNSRE.2020.3035836. Epub 2021 Jan 28.
Automatic seizure prediction promotes the development of closed-loop treatment system on intractable epilepsy. In this study, by considering the specific information exchange between EEG channels from the perspective of whole brain activities, the convolution neural network (CNN) and the directed transfer function (DTF) were merged to present a novel method for patient-specific seizure prediction. Firstly, the intracranial electroencephalogram (iEEG) signals were segmented and the information flow features of iEEG signals were calculated by using the DTF algorithm. Then, these features were reconstructed as the channel-frequency maps according to channel pairs and the frequency of information flow. Finally, these maps were fed into the CNN model and the outputs were post-processed by the moving average approach to predict the epileptic seizures. By the evaluation of cross-validation method, the proposed algorithm achieved the averaged sensitivity of 90.8%, the averaged false prediction rate of 0.08 per hour. Compared to the random predictor and other existing algorithms tested on the Freiburg EEG dataset, our proposed method achieved better performance for seizure prediction in all patients. These results demonstrated that the proposed algorithm could provide an robust seizure prediction solution by using deep learning to capture the brain network changes of iEEG signals from epileptic patients.
自动癫痫发作预测促进了难治性癫痫闭环治疗系统的发展。在这项研究中，通过从全脑活动的角度考虑 EEG 通道之间的特定信息交换，卷积神经网络 (CNN) 和有向传递函数 (DTF) 被合并，提出了一种用于患者特异性癫痫发作预测的新方法。首先，对颅内脑电图 (iEEG) 信号进行分段，并使用 DTF 算法计算 iEEG 信号的信息流特征。然后，根据通道对和信息流的频率，将这些特征重构为通道-频率图。最后，将这些图输入 CNN 模型，并通过移动平均方法对输出进行后处理，以预测癫痫发作。通过交叉验证方法的评估，所提出的算法在所有患者中的平均灵敏度达到 90.8%，平均错误预测率为每小时 0.08。与在弗莱堡 EEG 数据集上测试的随机预测器和其他现有算法相比，我们提出的方法在所有患者的癫痫发作预测中都表现出了更好的性能。这些结果表明，该算法可以通过使用深度学习来捕捉癫痫患者 iEEG 信号的脑网络变化，提供一种稳健的癫痫发作预测解决方案。
