Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural network (CNN) is proposed to diagnose the preictal period. Our goal is for those epileptic patients in whom seizures occur late and it is very challenging to record the preictal signal for them. In the previous works, generalized methods were inevitably used for this group of patients which were not very accurate. Our approach to solve this problem is to provide a few-shot learning method. This method, having the previous knowledge, is trained with only a small number of samples, learns new tasks and reduces the efforts to collect more data. Evaluation results for three patients from the CHB–MIT database, for a 10-min seizure prediction horizon (SPH) and a 20-min seizure occurrence period (SOP), averaged sensitivity of 95.70% and a false prediction rate (FPR) of 0.057/h and for the 5-min prediction horizon and the 25-min seizure occurrence period averaged sensitivity of 98.52% and a false prediction rate of (FPR) of 0.045/h. The proposed few-shot learning method, based on previous knowledge gained from the generalizable method, is regulated with a few new patient samples for the patient. Our results show that the accuracy obtained in this method is higher than the generalizable methods.

中文翻译：

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小样本学习法预测癫痫发作

癫痫发作预测和及时警报使患者能够采取有效的预防措施。在本文中，提出了一种卷积神经网络 (CNN) 来诊断发作前期。我们的目标是那些癫痫发作发生较晚并且为他们记录发作前信号非常具有挑战性的癫痫患者。在以往的工作中，对这组患者不可避免地使用了泛化的方法，并不是很准确。我们解决这个问题的方法是提供一种小样本学习方法。这种具有先前知识的方法仅使用少量样本进行训练，学习新任务并减少收集更多数据的工作量。来自 CHB-MIT 数据库的三名患者的评估结果，针对 10 分钟癫痫发作预测范围 (SPH) 和 20 分钟癫痫发作发生期 (SOP)，95.70% 的平均灵敏度和 0.057/h 的错误预测率 (FPR) 以及 5 分钟预测范围和 25 分钟癫痫发作发生期的平均灵敏度 98.52% 和 (FPR) 0.045/h 的错误预测率H。所提出的少样本学习方法基于从可推广方法中获得的先前知识，并通过患者的一些新患者样本进行调节。我们的结果表明，该方法获得的准确性高于可推广的方法。为患者提供一些新的患者样本进行监管。我们的结果表明，该方法获得的准确性高于可推广的方法。为患者提供一些新的患者样本进行监管。我们的结果表明，该方法获得的准确性高于可推广的方法。