| Citation: | Ye YUAN, Kebin JIA, Pengyu LIU. Multi-context Autoencoders for Multivariate Medical Signals Based on Deep Convolutional Neural Networks[J]. Journal of Electronics & Information Technology, 2020, 42(2): 371-378. doi: 10.11999/JEIT190135
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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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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
|
|
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
|
|
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
|
|
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
|
|
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.
|
|
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.
|
|
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
|
|
HOLZINGER A. Machine Learning for Health Informatics[M]. Cham: Springer, 2016: 161–182.
|
|
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.
|
|
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.
|
|
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.
|
|
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
|
|
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
|
|
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.
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
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.
|
|
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
|
|
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.
|
|
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.
|
|
SHOEB A H. Application of machine learning to epileptic seizure onset detection and treatment[D]. [Ph.D. dissertation], Massachusetts Institute of Technology, 2009.
|
|
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
|
|
FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861–874. doi: 10.1016/j.patrec.2005.10.010
|
|
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.
|
|
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
|
|
ZEILER M D. ADADELTA: An adaptive learning rate method[J]. arXiv preprint arXiv:1212.5701, 2012.
|
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