论文标题
成像一个单个引用的EEG电极的时间序列进行癫痫发作风险分析
Imaging the time series of one single referenced EEG electrode for Epileptic Seizures Risk Analysis
论文作者
论文摘要
由单一头皮电极（加上参考电极）捕获的时间序列用于预测癫痫发作的敏感性。使用三种已知的方法：复发图，Gramian Angular Field，Markov Transition Firt Field Field Field Field Field Field Field，将时间序列进行了预处理，分割，每个段转换为图像。通过平均CNN的SoftMax层的输出来计算，在未来预定义的时间窗口中发生癫痫发作的可能性与通常考虑分类层输出的情况不同。通过阈值这种可能性，癫痫发作的预测具有更好的性能。有趣的是，对于几乎每个患者，最佳阈值与50％不同。结果表明，该技术可以预测一些癫痫发作和患者的良好结果。但是，需要更多的测试，即更多的患者和更多的癫痫发作，以更好地了解该技术的真正潜力。
The time series captured by a single scalp electrode (plus the reference electrode) of refractory epileptic patients is used to forecast seizures susceptibility. The time series is preprocessed, segmented, and each segment transformed into an image, using three different known methods: Recurrence Plot, Gramian Angular Field, Markov Transition Field. The likelihood of the occurrence of a seizure in a future predefined time window is computed by averaging the output of the softmax layer of a CNN, differently from the usual consideration of the output of the classification layer. By thresholding this likelihood, seizure forecasting has better performance. Interestingly, for almost every patient, the best threshold was different from 50%. The results show that this technique can predict with good results for some seizures and patients. However, more tests, namely more patients and more seizures, are needed to better understand the real potential of this technique.
