高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于深度卷积神经网络的多元医学信号多级上下文自编码器

袁野 贾克斌 刘鹏宇

袁野, 贾克斌, 刘鹏宇. 基于深度卷积神经网络的多元医学信号多级上下文自编码器[J]. 电子与信息学报, 2020, 42(2): 371-378. doi: 10.11999/JEIT190135
引用本文: 袁野, 贾克斌, 刘鹏宇. 基于深度卷积神经网络的多元医学信号多级上下文自编码器[J]. 电子与信息学报, 2020, 42(2): 371-378. doi: 10.11999/JEIT190135
Ye YUAN, Kebin JIA, Pengyu LIU. Multi-context Autoencoders for Multivariate Medical Signals Based on Deep Convolutional Neural Networks[J]. Journal of Electronics and Information Technology, 2020, 42(2): 371-378. doi: 10.11999/JEIT190135
Citation: Ye YUAN, Kebin JIA, Pengyu LIU. Multi-context Autoencoders for Multivariate Medical Signals Based on Deep Convolutional Neural Networks[J]. Journal of Electronics and Information Technology, 2020, 42(2): 371-378. doi: 10.11999/JEIT190135

基于深度卷积神经网络的多元医学信号多级上下文自编码器

doi: 10.11999/JEIT190135
基金项目: 国家自然科学基金(81871394),先进信息网络北京实验室基金(040000546618017)
详细信息
    作者简介:

    袁野:男,1991年生,博士生,研究方向为深度学习、健康信息学

    贾克斌:男,1962年生,教授,研究方向为多媒体信息系统、模式识别

    刘鹏宇:女,1979年生,副教授,研究方向为多媒体信息系统

    通讯作者:

    贾克斌 kebinj@bjut.edu.cn

  • 中图分类号: TP391.4

Multi-context Autoencoders for Multivariate Medical Signals Based on Deep Convolutional Neural Networks

Funds: The National Natural Science Foundation of China (81871394), The Beijing Laboratory of Advanced Information Networks Foundation (040000546618017)
  • 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。
  • 图  1  本文提出的多级上下文深度卷积自编码器结构图

    图  2  不同特征表示模型在CHB-MIT和UCD数据库上的ROC和PR曲线

    图  3  不同特征学习模型在CHB-MIT数据库上对不同超参数配置的影响

    图  4  不同特征学习模型在UCD数据库上对不同超参数配置对的影响

    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$1 \times 2$
    下载: 导出CSV

    表  2  CHB-MIT数据库上的方法比较结果

    方法AUC-ROCAUC-PRF1分子准确率
    PCA0.8291 ± 0.04340.7021 ± 0.08720.6421 ± 0.02230.8768 ± 0.0223
    SAE0.5934 ± 0.03770.4180 ± 0.11890.0668 ± 0.04150.7987 ± 0.0309
    CAE0.8657 ± 0.03050.7646 ± 0.08810.6277 ± 0.12460.8690 ± 0.0267
    Med2Vec0.8155 ± 0.11810.5870 ± 0.16700.6066 ± 0.23630.8351 ± 0.0359
    Skip-gram+0.9090 ± 0.03560.7467 ± 0.15400.6288 ± 0.20400.8898 ± 0.0173
    CtxFusionEEG0.9287 ± 0.03060.7833 ± 0.11470.7202 ± 0.14850.9025 ± 0.0104
    Wave2Vec0.9035 ± 0.03710.8839 ± 0.02610.8267 ± 0.01840.9210 ± 0.0099
    m-CAE0.8946 ± 0.04010.8727 ± 0.01890.8417 ± 0.01310.9324 ± 0.0058
    mCtx-CAE0.9372 ± 0.04950.8980 ± 0.03330.8493 ± 0.01910.9412 ± 0.0110
    下载: 导出CSV

    表  3  UCD数据库上的方法比较结果

    方法AUC-ROCAUC-PRF1分数准确率
    PCA0.8177 ± 0.01420.5764 ± 0.01720.5204 ± 0.02750.6193 ± 0.0638
    SAE0.7068 ± 0.13720.4965 ± 0.09510.2760 ± 0.18150.4917 ± 0.1364
    CAE0.8386 ± 0.03760.5710 ± 0.04290.5180 ± 0.07010.6208 ± 0.0961
    Med2Vec0.7479 ± 0.07960.4836 ± 0.10460.3997 ± 0.13610.5619 ± 0.0619
    Skip-gram+0.8010 ± 0.09920.5406 ± 0.09950.4342 ± 0.17310.5884 ± 0.1077
    CtxFusionEEG0.7941 ± 0.14850.6358 ± 0.07090.5171 ± 0.19940.6375 ± 0.1074
    Wave2Vec0.8161 ± 0.05070.5984 ± 0.06980.5268 ± 0.06610.6408 ± 0.0723
    m-CAE0.8446 ± 0.03610.5727 ± 0.02150.5600 ± 0.04820.6562 ± 0.0767
    mCtx-CAE0.8648 ± 0.02580.6423 ± 0.04520.5655 ± 0.02280.6734 ± 0.0562
    下载: 导出CSV
  • [1] JOHNSON A E W, GHASSEMI M M, NEMATI S, et al. Machine learning and decision support in critical care[J]. Proceedings of the IEEE, 2016, 104(2): 444–466. doi:  10.1109/JPROC.2015.2501978
    [2] RAVI D, WONG C, DELIGIANNI F, et al. Deep learning for health informatics[J]. IEEE Journal of Biomedical and Health Informatics, 2017, 21(1): 4–21. doi:  10.1109/JBHI.2016.2636665
    [3] BOOSTANI R, KARIMZADEH F, and NAMI M. A comparative review on sleep stage classification methods in patients and healthy individuals[J]. Computer Methods and Programs in Biomedicine, 2017, 140: 77–91. doi:  10.1016/j.cmpb.2016.12.004
    [4] YUAN Ye, XUN Guangxu, JIA Kebin, et al. A multi-view deep learning framework for EEG seizure detection[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(1): 83–94. doi:  10.1109/JBHI.2018.2871678
    [5] ACAR E, LEVIN-SCHWARTZ Y, CALHOUN V D, et al. Tensor-based fusion of EEG and fMRI to understand neurological changes in schizophrenia[C]. 2017 IEEE International Symposium on Circuits and Systems, Baltimore, USA, 2017: 1–4.
    [6] JIA Xiaowei, LI Kang, LI Xiaoyi, et al. A novel semi-supervised deep learning framework for affective state recognition on EEG signals[C]. 2014 IEEE International Conference on Bioinformatics and Bioengineering, Boca Raton, USA, 2014: 30–37.
    [7] LÄNGKVIST M, KARLSSON L, and LOUTFI A. A review of unsupervised feature learning and deep learning for time-series modeling[J]. Pattern Recognition Letters, 2014, 42: 11–24. doi:  10.1016/j.patrec.2014.01.008
    [8] HOLZINGER A. Machine Learning for Health Informatics[M]. Cham: Springer, 2016: 161–182.
    [9] SUPRATAK A, LI Ling, and GUO Yike. Feature extraction with stacked autoencoders for epileptic seizure detection[C]. The 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 2014: 4184–4187.
    [10] YAN Bo, WANG Yong, LI Yuheng, et al. An EEG signal classification method based on sparse auto-encoders and support vector machine[C]. 2016 IEEE/CIC International Conference on Communications in China, Chengdu, China, 2016: 1–6.
    [11] LIN Qin, YE Shuqun, HUANG Xiumei, et al. Classification of epileptic EEG signals with stacked sparse autoencoder based on deep learning[C]. The 12th International Conference on Intelligent Computing, Lanzhou, China, 2016: 802–810.
    [12] YANG Jianli, BAI Yang, LI Guojun, et al. A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression[J]. Bio-medical Materials and Engineering, 2015, 26(S1): S1549–S1558. doi:  10.3233/BME-151454
    [13] XUN Guangxu, JIA Xiaowei, and ZHANG Aidong. Detecting epileptic seizures with electroencephalogram via a context-learning model[J]. BMC Medical Informatics and Decision Making, 2016, 16(Suppl 2): 70. doi:  10.1186/s12911-016-0310-7
    [14] LI Xiaoyi, JIA Xiaowei, XUN Guangxu, et al. Improving EEG feature learning via synchronized facial video[C]. 2015 IEEE International Conference on Big Data, Santa Clara, USA, 2015: 843–848.
    [15] YUAN Ye, XUN Guangxu, SUO Qiuling, et al. Wave2Vec: Deep representation learning for clinical temporal data[J]. Neurocomputing, 2019, 324: 31–42. doi:  10.1016/j.neucom.2018.03.074
    [16] YUAN Ye, XUN Guangxu, JIA Kebin, et al. A multi-context learning approach for EEG epileptic seizure detection[J]. BMC Systems Biology, 2018, 12(6): 47–57. doi:  10.1186/s12918-018-0626-2
    [17] ZHANG Junming, WU Yan, BAI Jing, et al. Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers[J]. Transactions of the Institute of Measurement and Control, 2016, 38(4): 435–451. doi:  10.1177/0142331215587568
    [18] YULITA I N, FANANY M I, and ARYMURTHY A M. Sequence-based sleep stage classification using conditional neural fields[J]. arXiv preprint arXiv:1610.01935 , 2016.
    [19] LÄNGKVIST M, KARLSSON L, and LOUTFI A. Sleep stage classification using unsupervised feature learning[J]. Advances in Artificial Neural Systems, 2012, 2012: 107046. doi:  10.1155/2012/107046
    [20] MASCI J, MEIER U, CIREŞAN D, et al. Stacked convolutional auto-encoders for hierarchical feature extraction[C]. The 21st International Conference on Artificial Neural Networks, Espoo, Finland, 2011: 52–59.
    [21] HINTON G E and SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504–507. doi:  10.1126/science.1127647
    [22] MIKOLOV T, SUTSKEVER I, CHEN Kai, et al. Distributed representations of words and phrases and their compositionality[C]. The 26th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2013: 3111–3119.
    [23] CHOI E, BAHADORI M T, SEARLES E, et al. Multi-layer representation learning for medical concepts[C]. The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, 2016: 1495–1504.
    [24] SHOEB A H. Application of machine learning to epileptic seizure onset detection and treatment[D]. [Ph.D. dissertation], Massachusetts Institute of Technology, 2009.
    [25] GOLDBERGER A L, AMARAL L A N, GLASS L, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals[J]. Circulation, 2000, 101(23): E215–E220. doi:  10.1161/01.CIR.101.23.e215
    [26] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861–874. doi:  10.1016/j.patrec.2005.10.010
    [27] DAVIS J and GOADRICH M. The relationship between precision-recall and ROC curves[C]. The 23rd International Conference on Machine Learning, Pittsburgh, USA, 2006: 233–240.
    [28] HE Haibo and GARCIA E A. Learning from imbalanced data[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263–1284. doi:  10.1109/TKDE.2008.239
    [29] ZEILER M D. ADADELTA: An adaptive learning rate method[J]. arXiv preprint arXiv:1212.5701, 2012.
  • [1] 陈怡, 唐迪, 邹维.  基于深度学习的Android恶意软件检测:成果与挑战, 电子与信息学报. doi: 10.11999/JEIT200009
    [2] 申滨, 王欣, 陈思吉, 崔太平.  基于机器学习主用户发射模式分类的蜂窝认知无线电网络频谱感知, 电子与信息学报. doi: 10.11999/JEIT191012
    [3] 申铉京, 沈哲, 黄永平, 王玉.  基于非局部操作的深度卷积神经网络车位占用检测算法, 电子与信息学报. doi: 10.11999/JEIT190349
    [4] 柯丽, 王丹妮, 杜强, 姜楚迪.  基于卷积长短时记忆网络的心律失常分类方法, 电子与信息学报. doi: 10.11999/JEIT190712
    [5] 付晓薇, 杨雪飞, 陈芳, 李曦.  一种基于深度学习的自适应医学超声图像去斑方法, 电子与信息学报. doi: 10.11999/JEIT190580
    [6] 谢金宝, 侯永进, 康守强, 李佰蔚, 张霄.  基于语义理解注意力神经网络的多元特征融合中文文本分类, 电子与信息学报. doi: 10.11999/JEIT170815
    [7] 郭立民, 寇韵涵, 陈涛, 张明.  基于栈式稀疏自编码器的低信噪比下低截获概率雷达信号调制类型识别, 电子与信息学报. doi: 10.11999/JEIT170588
    [8] 刘勤让, 刘崇阳.  利用参数稀疏性的卷积神经网络计算优化及其FPGA加速器设计, 电子与信息学报. doi: 10.11999/JEIT170819
    [9] 吕晓琪, 吴凉, 谷宇, 张明, 李菁.  基于深度卷积神经网络的低剂量CT肺部去噪, 电子与信息学报. doi: 10.11999/JEIT170769
    [10] 吴震东, 王雅妮, 章坚武.  基于深度学习的污损指纹识别研究, 电子与信息学报. doi: 10.11999/JEIT161121
    [11] 伍家松, 达臻, 魏黎明, SENHADJILotfi, 舒华忠.  基于分裂基-2/(2a)FFT算法的卷积神经网络加速性能的研究, 电子与信息学报. doi: 10.11999/JEIT160357
    [12] 王海, 蔡英凤, 贾允毅, 陈龙, 江浩斌.  基于深度卷积神经网络的场景自适应道路分割算法, 电子与信息学报. doi: 10.11999/JEIT160329
    [13] 侯志强, 戴铂, 胡丹, 余旺盛, 陈晨, 范舜奕.  基于感知深度神经网络的视觉跟踪, 电子与信息学报. doi: 10.11999/JEIT151449
    [14] 王星, 周一鹏, 周东青, 陈忠辉, 田元荣.  基于深度置信网络和双谱对角切片的低截获概率雷达信号识别, 电子与信息学报. doi: 10.11999/JEIT160031
    [15] 李寰宇, 毕笃彦, 查宇飞, 杨源.  一种易于初始化的类卷积神经网络视觉跟踪算法, 电子与信息学报. doi: 10.11999/JEIT150600
    [16] 李祖贺, 樊养余, 王凤琴.  YUV空间中基于稀疏自动编码器的无监督特征学习, 电子与信息学报. doi: 10.11999/JEIT150557
    [17] 程帅, 孙俊喜, 曹永刚, 刘广文, 韩广良.  多示例深度学习目标跟踪, 电子与信息学报. doi: 10.11999/JEIT150319
    [18] 程帅, 曹永刚, 孙俊喜, 赵立荣, 刘广文, 韩广良.  基于增强群跟踪器和深度学习的目标跟踪, 电子与信息学报. doi: 10.11999/JEIT141362
    [19] 李寰宇, 毕笃彦, 杨源, 查宇飞, 覃兵, 张立朝.  基于深度特征表达与学习的视觉跟踪算法研究, 电子与信息学报. doi: 10.11999/JEIT150031
    [20] 孙志军, 薛磊, 许阳明.  基于深度学习的边际Fisher分析特征提取算法, 电子与信息学报. doi: 10.3724/SP.J.1146.2012.00949
  • 加载中
  • 图(4) / 表ll (3)
    计量
    • 文章访问数:  3085
    • HTML全文浏览量:  970
    • PDF下载量:  70
    • 被引次数: 0
    出版历程
    • 收稿日期:  2019-03-07
    • 修回日期:  2019-08-17
    • 网络出版日期:  2019-08-28
    • 刊出日期:  2020-02-01

    目录

      /

      返回文章
      返回

      官方微信,欢迎关注