Multi-context Autoencoders for Multivariate Medical Signals Based on Deep Convolutional Neural Networks
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摘要:
多元医学信号的典型代表有多模态睡眠图和多通道脑电图等，采用无监督深度学习表征多元医学信号是目前健康信息学领域中的一个研究热点。为了解决现有模型没有充分结合医学信号多元时序结构特点的问题，该文提出了一种无监督的多级上下文深度卷积自编码器(mCtx-CAE)。首先改进传统卷积神经网络结构，提出一种多元卷积自编码模块，以提取信号片段内的多元上下文特征；其次，提出采用语义学习技术对信号片段间的时序信息进行自编码，进一步提取时序上下文特征；最后通过共享特征表示设计目标函数，训练端到端的多级上下文自编码器。实验结果表明，该文所提模型在两种应用于不同医疗场景下的多模态和多通道数据集(UCD和CHB-MIT)上表现均优于其它无监督特征学习方法，能有效提高多元医学信号的融合特征表达能力，对提高临床时序数据的分析效率有着重要意义。
Abstract:Learning unsupervised representations from multivariate medical signals, such as multi-modality polysomnography and multi-channel electroencephalogram, has gained increasing attention in health informatics. In order to solve the problem that the existing models do not fully incorporate the characteristics of the multivariate-temporal structure of medical signals, an unsupervised multi-Context deep Convolutional AutoEncoder (mCtx-CAE) is proposed in this paper. Firstly, by modifying traditional convolutional neural networks, a multivariate convolutional autoencoder is proposed to extract multivariate context features within signal segments. Secondly, semantic learning is adopted to auto-encode temporal information among signal segments, to further extract temporal context features. Finally, an end-to-end multi-context autoencoder is trained by designing objective function based on shared feature representation. Experimental results conducted on two public benchmark datasets (UCD and CHB-MIT) show that the proposed model outperforms the state-of-the-art unsupervised feature learning methods in different medical tasks, demonstrating the effectiveness of the learned fusional features in clinical settings.
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表 1 多元卷积自编码模块具体配置参数
编码单元 卷积层 非线性变换 池化层 元内编码单元 $1 \times 3 \times 16$ ReLU $1 \times 2$ 元间编码单元 $C \times 3 \times 8$ ReLU $1 \times 2$ 解码单元 反卷积层 非线性变换 反池化层 元间解码单元 $C \times 3 \times 8$ ReLU $1 \times 2$ 元内解码单元 $1 \times 3 \times 16$ ReLU $1 \times 2$ 
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表 2 CHB-MIT数据库上的方法比较结果
方法 AUC-ROC AUC-PR F1分子 准确率 PCA 0.8291 ± 0.0434 0.7021 ± 0.0872 0.6421 ± 0.0223 0.8768 ± 0.0223 SAE 0.5934 ± 0.0377 0.4180 ± 0.1189 0.0668 ± 0.0415 0.7987 ± 0.0309 CAE 0.8657 ± 0.0305 0.7646 ± 0.0881 0.6277 ± 0.1246 0.8690 ± 0.0267 Med2Vec 0.8155 ± 0.1181 0.5870 ± 0.1670 0.6066 ± 0.2363 0.8351 ± 0.0359 Skip-gram+ 0.9090 ± 0.0356 0.7467 ± 0.1540 0.6288 ± 0.2040 0.8898 ± 0.0173 CtxFusionEEG 0.9287 ± 0.0306 0.7833 ± 0.1147 0.7202 ± 0.1485 0.9025 ± 0.0104 Wave2Vec 0.9035 ± 0.0371 0.8839 ± 0.0261 0.8267 ± 0.0184 0.9210 ± 0.0099 m-CAE 0.8946 ± 0.0401 0.8727 ± 0.0189 0.8417 ± 0.0131 0.9324 ± 0.0058 mCtx-CAE 0.9372 ± 0.0495 0.8980 ± 0.0333 0.8493 ± 0.0191 0.9412 ± 0.0110
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表 3 UCD数据库上的方法比较结果
方法 AUC-ROC AUC-PR F1分数 准确率 PCA 0.8177 ± 0.0142 0.5764 ± 0.0172 0.5204 ± 0.0275 0.6193 ± 0.0638 SAE 0.7068 ± 0.1372 0.4965 ± 0.0951 0.2760 ± 0.1815 0.4917 ± 0.1364 CAE 0.8386 ± 0.0376 0.5710 ± 0.0429 0.5180 ± 0.0701 0.6208 ± 0.0961 Med2Vec 0.7479 ± 0.0796 0.4836 ± 0.1046 0.3997 ± 0.1361 0.5619 ± 0.0619 Skip-gram+ 0.8010 ± 0.0992 0.5406 ± 0.0995 0.4342 ± 0.1731 0.5884 ± 0.1077 CtxFusionEEG 0.7941 ± 0.1485 0.6358 ± 0.0709 0.5171 ± 0.1994 0.6375 ± 0.1074 Wave2Vec 0.8161 ± 0.0507 0.5984 ± 0.0698 0.5268 ± 0.0661 0.6408 ± 0.0723 m-CAE 0.8446 ± 0.0361 0.5727 ± 0.0215 0.5600 ± 0.0482 0.6562 ± 0.0767 mCtx-CAE 0.8648 ± 0.0258 0.6423 ± 0.0452 0.5655 ± 0.0228 0.6734 ± 0.0562
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