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Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100).
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
2000
Earlier work this paper cites.
G. Moody and R. Mark, “The impact of the MIT-BIH Arrhythmia Database,” IEEE Engineering in Medicine and Biology Magazine
2001
Earlier work this paper cites.
G. Schalk, D. McFarland, T. Hinterberger, N. Birbaumer, and J. Wolpaw, “BCI2000: A general-purpose brain-computer interface (BCI) system,” IEEE Transactions on Biomedical Engineering
2004
Earlier work this paper cites.
P. de Chazal, M. O’Dwyer, and R. Reilly, “Automatic classification of heartbeats using ECG morphology and heartbeat interval features,” IEEE Transactions on Biomedical Engineering
2004
Earlier work this paper cites.
J. Healey and R. Picard, “Detecting stress during real-world driving tasks using physiological sensors,” IEEE Transactions on Intelligent Transportation Systems
2005
Earlier work this paper cites.
S. Dash, K. H. Chon, S. Lu, and E. A. Raeder, “Automatic real time detection of atrial fibrillation,” Annals of Biomedical Engineering
2009
Earlier work this paper cites.
M. Gutmann and A. Hyvarinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in International Conference on Artificial Intelligence and Statistics
2010
Earlier work this paper cites.
G. Doquire, G. de Lannoy, D. François, and M. Verleysen, “Feature selection for interpatient supervised heart beat classification,” Computational Intelligence and Neuroscience
2011
Earlier work this paper cites.
U. R. Acharya, S. Vinitha Sree, G. Swapna, R. J. Martis, and J. S. Suri, “Automated EEG analysis of epilepsy: A review,” Knowledge-Based Systems
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27
2014
Earlier work this paper cites.
H. Huang, J. Liu, Q. Zhu, R. Wang, and G. Hu, “A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals,” BioMedical Engineering OnLine
2014
Earlier work this paper cites.
C.-C. Lin and C.-M. Yang, “Heartbeat classification using normalized RR intervals and morphological features,” Mathematical Problems in Engineering
2014
Earlier work this paper cites.
E. Tzeng, J. Hoffman, T. Darrell, K. Saenko, and U. Lowell, “Simultaneous deep transfer across domains and tasks,” in The IEEE International Conference on Computer Vision
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” International Conference on Machine Learning
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” International Conference for Learning Representations
2015
Earlier work this paper cites.
I. Misra, C. L. Zitnick, and M. Hebert, “Shuffle and learn: Unsupervised learning using temporal order verification,” in European Conference on Computer Vision
2016
Earlier work this paper cites.
A. Hyvärinen and H. Morioka, “Unsupervised feature extraction by time-contrastive learning and nonlinear ICA,” in Advances in Neural Information Processing Systems 29
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in European Conference on Computer Vision
2016
Cited alongside, same era.
Y. Kim, J. Ryu, K. K. Kim, C. C. Took, D. P. Mandic, and C. Park, “Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns,” Computational Intelligence and Neuroscience
2016
Cited alongside, same era.
C. Doersch and A. Zisserman, “Multi-task self-supervised visual learning,” in 2017 IEEE International Conference on Computer Vision (ICCV)
2017
Cited alongside, same era.
F. S. de Aguiar Neto and J. L. G. Rosa, “Depression biomarkers using non-invasive EEG: A review,” Neuroscience & Biobehavioral Reviews
2019
Later among the works it cites.
2019
Later among the works it cites.
H. Banville, I. Albuquerque, A. Hyvärinen, G. Moffat, D.-A. Engemann, and A. Gramfort, “Self-supervised representation learning from electroencephalography signals,” in 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
2019
Later among the works it cites.
2019
Later among the works it cites.
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2017
Cited alongside, same era.
Q. Xie, Z. Dai, Y. Du, E. Hovy, and G. Neubig, “Controllable invariance through adversarial feature learning,” in Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. M. Krell and S. K. Kim, “Rotational data augmentation for electroencephalographic data,” in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
2017
Cited alongside, same era.
I. H. Bruun, S. M. S. Hissabu, E. S. Poulsen, and S. Puthusserypady, “Automatic atrial fibrillation detection: A novel approach using discrete wavelet transform and heart rate variability,” in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
2017
Cited alongside, same era.
G. Garcia, G. Moreira, D. Menotti, and E. Luz, “Inter-patient ECG heartbeat classification with temporal VCG optimized by PSO,” Scientific Reports
2017
Cited alongside, same era.
K. Luo, J. Li, Z. Wang, and A. Cuschieri, “Patient-specific deep architectural model for ECG classification,” Journal of Healthcare Engineering
2017
Cited alongside, same era.
D. Wei, J. Lim, A. Zisserman, and W. T. Freeman, “Learning and using the arrow of time,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018
Cited alongside, same era.
A. Farshchian, J. A. Gallego, L. E. Miller, S. A. Solla, J. P. Cohen, and Y. Bengio, “Adversarial domain adaptation for stable brain-machine interfaces,” International Conference on Learning Representations
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32
2019
Later among the works it cites.
Z. Wu, X. Feng, and C. Yang, “A deep learning method to detect atrial fibrillation based on continuous wavelet transform,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
2019
Later among the works it cites.
S. S. Xu, M.-W. Mak, and C.-C. Cheung, “Towards end-to-end ECG classification with raw signal extraction and deep neural networks,” IEEE Journal of Biomedical and Health Informatics
2019
Later among the works it cites.
J. Niu, Y. Tang, Z. Sun, and W. Zhang, “Inter-patient ECG classification with symbolic representations and multi-perspective convolutional neural networks,” IEEE Journal of Biomedical and Health Informatics
2019
Later among the works it cites.
Y. Cui, M. Jia, T.-Y. Lin, Y. Song, and S. Belongie, “Class-balanced loss based on effective number of samples,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019
Later among the works it cites.
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “CutMix: Regularization strategy to train strong classifiers with localizable features,” in International Conference on Computer Vision
2019
Later among the works it cites.
P. Sarkar and A. Etemad, “Self-supervised learning for ECG-based emotion recognition,” ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2020
Closest in time.
2020
Closest in time.
O. Ozdenizci, Y. Wang, T. Koike-Akino, and D. Erdogmus, “Learning invariant representations from EEG via adversarial inference,” IEEE Access
2020
Closest in time.
“PhysioNet: EEG motor movement/imagery dataset.” https://physionet.org/content/eegmmidb/1.0.0/ , 2009 (last accessed June 2, 2020)
2020
Closest in time.
“PhysioNet: MIT-BIH arrhythmia database.” https://physionet.org/content/mitdb/1.0.0/ , 2005 (last accessed June 2, 2020)
2020
Closest in time.