Abstract：
Epileptic seizures are unpredictable events caused by abnormal discharges of a patient\'s brain cells. Extensive research has been conducted to develop seizure prediction algorithms based on long-term continuous electroencephalogram (EEG) signals. This paper describes a patient-specific seizure prediction method that can serve as a basis for the design of lightweight, wearable and effective seizure-prediction devices. We aim to achieve two objectives using this method. The first aim is to extract robust feature representations from multichannel EEG signals, and the second aim is to reduce the number of channels used for prediction by selecting an optimal set of channels from multichannel EEG signals while ensuring good prediction performance. We design a seizure-prediction algorithm based on a vision transformer (ViT) model. The algorithm selects channels that play a key role in seizure prediction from 22 channels of EEG signals. First, we perform a time-frequency analysis of processed time-series signals to obtain EEG spectrograms. We then segment the spectrograms of multiple channels into many non-overlapping patches of the same size, which are input into the channel selection layer of the proposed model, named Sel-JPM-ViT, enabling it to select channels. Application of the Sel-JPM-ViT model to the Boston Children\'s Hospital-Massachusetts Institute of Technology scalp EEG dataset yields results using only three to six channels of EEG signals that are slightly better that the results obtained using 22 channels of EEG signals. Overall, the Sel-JPM-ViT model exhibits an average classification accuracy of 93.65%, an average sensitivity of 94.70% and an average specificity of 92.78%.
摘要：
癫痫发作是由患者脑细胞异常放电引起的不可预测的事件。已经进行了广泛的研究以开发基于长期连续脑电图（EEG）信号的癫痫发作预测算法。本文介绍了一种针对患者的癫痫发作预测方法，该方法可以作为轻量级设计的基础，可穿戴和有效的癫痫发作预测设备。我们使用这种方法来实现两个目标。第一个目标是从多通道EEG信号中提取鲁棒的特征表示，第二个目的是通过从多通道EEG信号中选择一组最佳通道来减少用于预测的通道数量，同时确保良好的预测性能。我们设计了一种基于视觉变换器（ViT）模型的癫痫发作预测算法。该算法从22个通道的脑电信号中选择在癫痫发作预测中起关键作用的通道。首先,我们对处理后的时间序列信号进行时频分析，以获得EEG频谱图。然后，我们将多个通道的频谱图分割成许多相同大小的非重叠斑块，它们被输入到所提出的模型的信道选择层，名为Sel-JPM-ViT,使其能够选择频道。将Sel-JPM-ViT模型应用于波士顿儿童医院-麻省理工学院头皮脑电图数据集，仅使用三到六个通道的脑电图信号得出的结果略好于使用22通道的脑电图信号获得的结果。总的来说,Sel-JPM-ViT模型的平均分类准确率为93.65%，平均灵敏度为94.70%，平均特异度为92.78%。
