Nazari Jamal, Motie Nasrabadi Ali, Menhaj Mohammad Bagher, Raiesdana Somayeh
Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Brain Inform. 2022 Sep 16;9(1):21. doi: 10.1186/s40708-022-00170-8.
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.
癫痫发作预测和及时报警能让患者采取有效预防措施。本文提出一种卷积神经网络（CNN）来诊断发作前期。我们的目标是针对那些发作较晚且记录其发作前期信号极具挑战性的癫痫患者。在以往的研究中，不可避免地对这类患者使用了不太准确的通用方法。我们解决此问题的方法是提供一种少样本学习方法。该方法利用先前知识，仅通过少量样本进行训练，学习新任务并减少收集更多数据的工作量。对CHB - MIT数据库中的三名患者进行评估，对于10分钟的发作预测期（SPH）和20分钟的发作发生期（SOP），平均灵敏度为95.70%，误预测率（FPR）为0.057/小时；对于5分钟的预测期和25分钟的发作发生期，平均灵敏度为98.52%，误预测率（FPR）为0.045/小时。所提出的少样本学习方法基于从通用方法获得的先前知识，用针对该患者的少量新患者样本进行调整。我们的结果表明，此方法获得的准确率高于通用方法。
