Wang Xiaoshuang, Zhang Guanghui, Wang Ying, Yang Lin, Liang Zhanhua, Cong Fengyu
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, P. R. China.
Faculty of Information Technology, University of Jyväskylä, Jyväskylä 40014, Finland.
Int J Neural Syst. 2022 Feb;32(2):2150048. doi: 10.1142/S0129065721500489. Epub 2021 Oct 12.
Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30[Formula: see text]min and seizure prediction horizon (SPH) of 5[Formula: see text]min, 98.60[Formula: see text] accuracy, 98.85[Formula: see text] sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60[Formula: see text]min and SPH of 5[Formula: see text]min, 98.32[Formula: see text] accuracy, 98.48[Formula: see text] sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.
近年来，利用颅内脑电图（iEEG）进行癫痫发作预测受到了越来越多的关注。iEEG信号通常以多通道的形式记录。许多先前的研究通常使用所有通道的iEEG信号来预测癫痫发作，而忽略了通道选择的考量。在本研究中，提出了一种结合通道选择策略的一维卷积神经网络（1D-CNN）方法用于癫痫发作预测。首先，我们使用30秒的滑动窗口对原始iEEG信号进行分割。然后，将处于三种通道形式（单通道、仅来自癫痫发作起始或无发作区的通道以及来自癫痫发作起始和无发作区的所有通道）的30秒iEEG片段用作1D-CNN的输入进行分类，并训练患者特异性模型。最后，为每个患者选择分类效果最佳的通道形式。所提出的方法在弗莱堡医院iEEG数据集上进行了评估。在癫痫发作期（SOP）为30分钟且癫痫发作预测时限（SPH）为5分钟的情况下，准确率达到98.60%，灵敏度达到98.85%，假预测率（FPR）为0.01/小时。在SOP为60分钟且SPH为5分钟的情况下，准确率达到98.32%，灵敏度达到98.48%，FPR为0.01/小时。与使用相同iEEG数据集的许多现有方法相比，我们的方法表现出更好的性能。
