About 1% of the population around the world suffers from epilepsy. The success of epilepsy surgery depends critically on pre-operative localization of epileptogenic zones. High frequency oscillations including ripples (80-250 Hz) and fast ripples (250-500 Hz) are commonly used as biomarkers to localize epileptogenic zones. Recent literature demonstrated that fast ripples indicate epileptogenic zones better than ripples. Thus, it is crucial to accurately detect fast ripples from ripples signals of magnetoencephalography for improving outcome of epilepsy surgery. This paper proposes an automatic and accurate ripple and fast ripple detection method that employs virtual sample generation and neural networks with an attention mechanism. We evaluate our proposed detector on patient data with 50 ripples and 50 fast ripples labeled by two experts. The experimental results show that our new detector outperforms multiple traditional machine learning models. In particular, our method can achieve a mean accuracy of 89.3% and an average area under the receiver operating characteristic curve of 0.88 in 50 repeats of random subsampling validation. In addition, we experimentally demonstrate the effectiveness of virtual sample generation, attention mechanism, and architecture of neural network models.

中文翻译：

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通过虚拟样本生成和注意力神经网络自动准确地进行癫痫纹波和快速纹波检测。

世界各地约有1％的人口患有癫痫病。癫痫手术的成功关键取决于术前癫痫发生区的定位。包括脉动（80-250 Hz）和快速脉动（250-500 Hz）在内的高频振荡通常被用作定位癫痫发生区的生物标记。最近的文献表明，快速波纹比波纹更好地表明了癫痫发生区。因此，准确地从脑磁图的波动信号中检测出快速波动对于改善癫痫手术的结局至关重要。本文提出了一种自动准确的波动和快速波动检测方法，该方法利用虚拟样本生成和具有注意机制的神经网络。我们根据两名专家标记的具有50个波纹和50个快速波纹的患者数据评估我们建议的检测器。实验结果表明，我们的新型检测器优于多种传统的机器学习模型。特别地，我们的方法在随机次采样验证的50次重复中可以达到89.3％的平均准确度和0.88的接收器工作特性曲线下的平均面积。此外，我们通过实验证明了虚拟样本生成，注意力机制和神经网络模型体系结构的有效性。