A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representation employed in traditional machine learning based solutions. The seizure detection system utilises only convolutional layers in order to process the multichannel time domain signal and is designed to exploit the large amount of weakly labelled data in the training stage. The system performance is assessed on a large database of continuous EEG recordings of 834h in duration; this is further validated on a held-out publicly available dataset and compared with two baseline SVM based systems. The developed system achieves a 56% relative improvement with respect to a feature-based state-of-the art baseline, reaching an AUC of 98.5%; this also compares favourably both in terms of performance and run-time. The effect of varying architectural parameters is thoroughly studied. The performance improvement is achieved through novel architecture design which allows more efficient usage of available training data and end-to-end optimisation from the front-end feature extraction to the back-end classification. The proposed architecture opens new avenues for the application of deep learning to neonatal EEG, where the performance becomes a function of the amount of training data with less dependency on the availability of precise clinical labels.

中文翻译：

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使用完全卷积结构从原始多通道脑电图检测新生儿癫痫发作。

提出了一种用于检测新生儿癫痫发作的深度学习分类器。该体系结构旨在检测原始脑电图（EEG）信号中的癫痫发作事件，这与传统的基于机器学习的解决方案中采用的最新的手工工程基于特征的表示方法不同。癫痫发作检测系统仅利用卷积层来处理多通道时域信号，并设计为在训练阶段利用大量弱标记数据。系统性能在一个持续时间为834h的连续EEG记录的大型数据库中进行评估；在保留的公共可用数据集上对此进行了进一步验证，并与两个基于基线SVM的系统进行了比较。与基于功能的最新基准相比，开发的系统实现了56％的相对改进，达到98.5％的AUC; 在性能和运行时间方面，这也都是有利的。彻底研究了变化的建筑参数的影响。通过新颖的体系结构设计可以实现性能提高，该体系结构允许更有效地使用可用的训练数据以及从前端特征提取到后端分类的端到端优化。拟议的体系结构为将深度学习应用于新生儿EEG开辟了新途径，其中，该性能成为训练数据量的函数，而对精确临床标签的可用性的依赖性降低。通过新颖的体系结构设计可以实现性能提高，该体系结构允许更有效地使用可用的训练数据以及从前端特征提取到后端分类的端到端优化。拟议的体系结构为将深度学习应用于新生儿EEG开辟了新途径，其中，该性能成为训练数据量的函数，而对精确临床标签的可用性的依赖性降低。通过新颖的体系结构设计可以实现性能提高，该体系结构允许更有效地使用可用的训练数据以及从前端特征提取到后端分类的端到端优化。拟议的体系结构为将深度学习应用于新生儿EEG开辟了新途径，其中，该性能成为训练数据量的函数，而对精确临床标签的可用性的依赖性降低。