OrShea Alison, Lightbody Gordon, Boylan Geraldine, Temko Andriy
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5862-5865. doi: 10.1109/EMBC.2018.8513617.
This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architectures of different depths were designed and tested; varying network depth impacts convolutional receptive fields and the corresponding learned feature complexity. Two deep convolutional networks are compared with a shallow SVMbased neonatal seizure detector, which relies on the extraction of hand-crafted features. On a large clinical dataset, of over 800 hours of multichannel unedited EEG, containing 1389 seizure events, the deep 11-layer architecture significantly outperforms the shallower architectures, improving the AUC90 from 82.6% to 86.8%. Combining the end-to-end deep architecture with the feature-based shallow SVM further improves the AUC90 to 87.6%. The fusion of classifiers of different depths gives greatly improved performance and reduced variability, making the combined classifier more clinically reliable.
本研究提出了一种新颖的、深度的、全卷积架构，该架构针对基于脑电图的新生儿癫痫检测任务进行了优化。设计并测试了不同深度的架构；不同的网络深度会影响卷积感受野以及相应学习到的特征复杂度。将两个深度卷积网络与一个基于浅层支持向量机的新生儿癫痫检测器进行比较，后者依赖于手工特征的提取。在一个包含超过800小时多通道未编辑脑电图、有1389次癫痫发作事件的大型临床数据集上，深度11层架构显著优于较浅的架构，将AUC90从82.6%提高到86.8%。将端到端深度架构与基于特征的浅层支持向量机相结合，进一步将AUC90提高到87.6%。不同深度分类器的融合极大地提高了性能并降低了变异性，使得组合分类器在临床上更可靠。
