Fetching the paper…
Reading the bibliography…
Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling.
J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE transactions on biomedical engineering , no. 3, pp. 230–236, 1985
1985
Earlier work this paper cites.
A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation , vol. 101, no. 23, pp. e215–e220, 2000 (June 13), circulation Electronic Pages: http://circ.ahajournals.org/content/101/23/e215.full PMID:1085218; doi: 10.1161/01.CIR.101.23.e215
2000
Earlier work this paper cites.
J. P. Martínez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, “A wavelet-based ECG delineator: evaluation on standard databases,” IEEE transactions on biomedical engineering , vol. 51, no. 4, pp. 570–581, 2004
2004
Earlier work this paper cites.
2013
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Earlier work this paper cites.
E. Choi, M. T. Bahadori, A. Schuetz, W. F. Stewart, and J. Sun, “Doctor AI: Predicting clinical events via recurrent neural networks,” in Machine Learning for Healthcare Conference , 2016, pp. 301–318
2016
Cited alongside, same era.
G. D. Clifford, C. Liu, B. Moody, L.-w. H. Lehman, I. Silva, Q. Li, A. Johnson, and R. G. Mark, “AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017,” Computing , vol. 44, p. 1, 2017
2017
Cited alongside, same era.
Z. Wang, W. Yan, and T. Oates, “Time series classification from scratch with deep neural networks: A strong baseline,” in Neural Networks (IJCNN), 2017 International Joint Conference on . IEEE, 2017, pp. 1578–1585
2017
Cited alongside, same era.
U. R. Acharya, H. Fujita, S. L. Oh, Y. Hagiwara, J. H. Tan, and M. Adam, “Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals,” Information Sciences , vol. 415, pp. 190–198, 2017
2017
C. Li, J. Zhu, and B. Zhang, “Max-margin deep generative models for (semi-) supervised learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2017
Cited alongside, same era.
H. Song, D. Rajan, J. J. Thiagarajan, and A. Spanias, “Attend and Diagnose: Clinical Time Series Analysis using Attention Models,” Proceedings of AAAI 2018 , 2018
2018
Closest in time.