Objective: Epilepsy is one of the most prevalent neurological diseases among humans and can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to mitigate injuries, and can be used to aid the treatment of patients with epilepsy. The purpose of a seizure prediction system is to successfully identify the pre-ictal brain stage, which occurs before a seizure event. Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem. However, little attention has been given for designing such models to adapt to the high inter-subject variability in EEG data. Methods: We propose two patient-independent deep learning architectures with different learning strategies that can learn a global function utilizing data from multiple subjects. Results: Proposed models achieve state-of-the-art performance for seizure prediction on the CHB-MIT-EEG dataset, demonstrating 88.81% and 91.54% accuracy respectively. Conclusions: The Siamese model trained on the proposed learning strategy is able to learn patterns related to patient variations in data while predicting seizures. Significance: Our models show superior performance for patient-independent seizure prediction, and the same architecture can be used as a patient-specific classifier after model adaptation. We are the first study that employs model interpretation to understand classifier behavior for the task for seizure prediction, and we also show that the MFCC feature map utilized by our models contains predictive biomarkers related to interictal and pre-ictal brain states.
翻译:目标:癫痫是人类中最流行的神经神经疾病之一,可导致严重的脑损伤、中风和脑肿瘤。早期发现缉获有助于减轻伤害,并可用于治疗癫痫病人。缉获预测系统的目的是成功确定在缉获事件之前发生的脑部前阶段。依赖病人的缉获预测模型旨在提供一个数据集中多个科目的准确性能,并被确定为缉获预测问题的一种真实世界解决办法。然而,对设计此类模型以适应EEEG数据中高对象间特征变异的模型很少给予注意。方法:我们提出两个依赖病人的深层次学习结构,这些结构具有不同的学习战略,能够利用多个主题的数据学习全球功能。结果:拟议模型在CHB-MIT-EEG数据集中达到缉获预测的状态性能,分别展示了88.81%和91.54%的准确性能。结论:在拟议的学习战略中经过培训的Siamese 模型能够学习到与病人之间特性变化有关的模式,以适应EEG数据,同时预测货币系统内部的预测性能表现。结果:我们所使用的生物分析模型可以显示我们用来进行生物分析的模型。