基于全卷积神经网络的多通道脑电图时序数据对新生儿缺氧缺血性脑病的分级

Shuwen Yu, William P. Marnane, Geraldine B. Boylan, Gordon Lightbody
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引用次数: 0

摘要

提出了一种用于新生儿缺氧缺血性脑病(HIE)分级的深度学习分类器。而不是使用手工制作的特征,这种架构可以输入原始的脑电图。在特征提取和分类块中都采用了全卷积层,这使得该体系结构更简单、更深入,但参数更少。本研究使用两个大型(分别为335 h和338 h)多中心新生儿连续脑电图数据集进行训练和测试。基于弱标签和通道独立性对模型进行训练。采用多数投票法对分类器结果(跨时间和通道)进行后处理,以增加预测的鲁棒性。使用降维工具UMAP可视化模型分类效果。该系统在大型未见测试集上的准确率为86.09%(95%置信区间为82.41-89.78%),MCC为0.7691,AUC为86.23%。选择两个利用时频分布特征的卷积神经网络架构作为基线,因为它们已经在相同的数据集上开发或测试过。与最佳基线相比,测试精度相对提高23.65%。此外,如果只有一个通道可用,与基于八个通道进行决策相比,测试精度仅降低2.63-5.91%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neonatal Hypoxic-Ischemic Encephalopathy Grading from Multi-Channel EEG Time-Series Data Using a Fully Convolutional Neural Network
A deep learning classifier is proposed for grading hypoxic-ischemic encephalopathy (HIE) in neonates. Rather than using handcrafted features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture simpler, and deeper, but with fewer parameters. Here, two large (335 h and 338 h, respectively) multi-center neonatal continuous EEG datasets were used for training and testing. The model was trained based on weak labels and channel independence. A majority vote method was used for the post-processing of the classifier results (across time and channels) to increase the robustness of the prediction. A dimension reduction tool, UMAP, was used to visualize the model classification effect. The proposed system achieved an accuracy of 86.09% (95% confidence interval: 82.41–89.78%), an MCC of 0.7691, and an AUC of 86.23% on the large unseen test set. Two convolutional neural network architectures which utilized time-frequency distribution features were selected as the baseline as they had been developed or tested on the same datasets. A relative improvement of 23.65% in test accuracy was obtained as compared with the best baseline. In addition, if only one channel was available, the test accuracy was only reduced by 2.63–5.91% compared with making decisions based on the eight channels.
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