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.

使用深度学习架构处理婴儿的脑电信号，并通过卷积层仅处理原始信号以检测癫痫发作，相较于传统的基于手工特征表达的机器学习方法，表现更优，同时可以更有效地利用可用的训练数据。