Epilepsy is the second most common neurological disorder, affecting 65 million people around the world. It is associated with seizures - a sudden, uncontrolled electrical disturbance in the brain that can lead to profound transient changes in behavior, movements, feelings, and levels of consciousness. Current approaches to developing a generalized automated seizure detection algorithm rely on constructing large, labeled training and test corpora of electroencephalograms (EEGs) from different individuals. However, due to the inherent inter-subject variability, heterogeneity of acquisition hardware, different montage choices, and various recording environments, EEG patterns may exhibit very different distributions over time and between individuals. Therefore, training an algorithm on such data without accounting for this diversity can affect the performance of any classifier or predictor. In addition, this process smooths out individual differences, producing a general, but a non-specific model. To address these issues, we propose a novel method that generates transferable features to interpolate between features from the training and test sets. This is achieved by adversarially training deep classifiers to make consistent classifications or predictions over the transferable features. Experiments on an EEG seizure databases demonstrate that the proposed method increases the accuracy over state-of-the-art from 86.83% to 91.71% and specificity from 87.38% to 94.73% while reducing the false positive rate/hour from 0.8/hour to 0.58/hour. Therefore, this work has the potential for significantly reducing workload in reviewing clinical EEGs for seizures, and for improved real-time closed-loop vagal stimulation.

中文翻译：

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使用生成可转移对抗性特征的通用癫痫检测模型


癫痫是第二常见的神经系统疾病，影响着全世界 6500 万人。它与癫痫发作有关，癫痫发作是一种突然的、不受控制的大脑电干扰，可能导致行为、运动、感觉和意识水平发生深刻的短暂变化。目前开发通用自动癫痫检测算法的方法依赖于构建来自不同个体的大型标记训练和测试脑电图（EEG）语料库。然而，由于固有的受试者间变异性、采集硬件的异质性、不同的蒙太奇选择和不同的记录环境，脑电图模式可能随着时间的推移和个体之间表现出非常不同的分布。因此，在不考虑这种多样性的情况下对此类数据训练算法可能会影响任何分类器或预测器的性能。此外，这个过程消除了个体差异，产生了一个通用但非特定的模型。为了解决这些问题，我们提出了一种新方法，可以生成可转移的特征以在训练集和测试集的特征之间进行插值。这是通过对抗性训练深度分类器以对可转移特征进行一致的分类或预测来实现的。 EEG 癫痫数据库实验表明，所提出的方法将最先进的准确度从 86.83% 提高到 91.71%，特异性从 87.38% 提高到 94.73%，同时将每小时的假阳性率从 0.8 个/小时降低到 0.58 个/小时。因此，这项工作有可能显着减少检查癫痫发作的临床脑电图的工作量，并改善实时闭环迷走神经刺激。