Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.

中文翻译：

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DWT-Net：卷积神经网络中具有结构化脑电蒙太奇和多特征提取器的癫痫发作检测系统

基于脑电图（EEG）的自动癫痫发作检测系统是计算机科学和神经科学之间的一个跨学科研究问题。癫痫病发作影响全世界1％的人口，并可能对安全和生活质量造成长期的严重危害。癫痫发作检测的自动化可以大大改善患者的治疗水平。在这项工作中，我们提出了一种神经网络模型，利用一种安排方法来提取脑电信号中的特征，这种方法是受传统神经病学家启发的。后处理器用于改善分类器的输出。我们在TUSZ数据集上的癫痫发作检测系统的结果达到了每24小时12次的误报率，灵敏度为59％，接近基于qEEG工具的普通人类检测器的性能。