Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks (2303.11338v3)

Published 20 Mar 2023 in eess.SP and cs.LG

Abstract: Despite their immense success in numerous fields, machine and deep learning systems have not yet been able to firmly establish themselves in mission-critical applications in healthcare. One of the main reasons lies in the fact that when models are presented with previously unseen, Out-of-Distribution samples, their performance deteriorates significantly. This is known as the Domain Generalization (DG) problem. Our objective in this work is to propose a benchmark for evaluating DG algorithms, in addition to introducing a novel architecture for tackling DG in biosignal classification. In this paper, we describe the Domain Generalization problem for biosignals, focusing on electrocardiograms (ECG) and electroencephalograms (EEG) and propose and implement an open-source biosignal DG evaluation benchmark. Furthermore, we adapt state-of-the-art DG algorithms from computer vision to the problem of 1D biosignal classification and evaluate their effectiveness. Finally, we also introduce a novel neural network architecture that leverages multi-layer representations for improved model generalizability. By implementing the above DG setup we are able to experimentally demonstrate the presence of the DG problem in ECG and EEG datasets. In addition, our proposed model demonstrates improved effectiveness compared to the baseline algorithms, exceeding the state-of-the-art in both datasets. Recognizing the significance of the distribution shift present in biosignal datasets, the presented benchmark aims at urging further research into the field of biomedical DG by simplifying the evaluation process of proposed algorithms. To our knowledge, this is the first attempt at developing an open-source framework for evaluating ECG and EEG DG algorithms.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (67)
  1. J. Wang et al., “Generalizing to unseen domains: A survey on domain generalization,” IEEE Transactions on Knowledge and Data Engineering, pp. 1–1, 2022.
  2. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, May 2015.
  3. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” pp. 770–778, 2016.
  4. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, June 2017.
  5. A. Vaswani et al., “Attention is all you need,” vol. 30, 2017.
  6. K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” pp. 1026–1034, 2015.
  7. V. Mnih et al., “Human-level control through deep reinforcement learning,” nature, vol. 518, no. 7540, pp. 529–533, February 2015.
  8. S. M. McKinney et al., “International evaluation of an AI system for breast cancer screening,” Nature, vol. 577, no. 7788, pp. 89–94, Jan. 2020.
  9. B. Recht, R. Roelofs, L. Schmidt, and V. Shankar, “Do ImageNet classifiers generalize to ImageNet?” in Proceedings of the 36th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97.   PMLR, June 2019, pp. 5389–5400.
  10. K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. C. Loy, “Domain generalization: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–20, 2022.
  11. G. Blanchard, G. Lee, and C. Scott, “Generalizing from several related classification tasks to a new unlabeled sample,” in Advances in Neural Information Processing Systems, J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, Eds., vol. 24.   Curran Associates, Inc., 2011.
  12. E. A. P. Alday et al., “Classification of 12-lead ECGs: the PhysioNet/computing in cardiology challenge 2020,” Physiological Measurement, vol. 41, no. 12, p. 124003, Dec. 2020.
  13. R.-N. Duan, J.-Y. Zhu, and B.-L. Lu, “Differential entropy feature for EEG-based emotion classification,” in 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013, pp. 81–84.
  14. W.-L. Zheng and B.-L. Lu, “Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks,” IEEE Transactions on Autonomous Mental Development, vol. 7, no. 3, pp. 162–175, 2015.
  15. A. Schaefer, F. Nils, X. Sanchez, and P. Philippot, “Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers,” Cognition and emotion, vol. 24, no. 7, pp. 1153–1172, 2010.
  16. W. Liu, W.-L. Zheng, Z. Li, S.-Y. Wu, L. Gan, and B.-L. Lu, “Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People,” Journal of Neural Engineering, vol. 19, no. 2, pp. 26–12, 2022.
  17. D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales, “Deeper, broader and artier domain generalization,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 5542–5550.
  18. A. Ballas and C. Diou, “Multi-layer representation learning for robust OOD image classification,” in Proceedings of the 12th Hellenic Conference on Artificial Intelligence, ser. SETN ’22.   New York, NY, USA: Association for Computing Machinery, 2022.
  19. ——, “A domain generalization approach for out-of-distribution 12-lead ECG classification with convolutional neural networks,” in 2022 IEEE Eighth International Conference on Big Data Computing Service and Applications (BigDataService).   Los Alamitos, CA, USA: IEEE Computer Society, aug 2022, pp. 9–13.
  20. Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in Proceedings of the 32nd International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, F. Bach and D. Blei, Eds., vol. 37.   Lille, France: PMLR, 07–09 Jul 2015, pp. 1180–1189.
  21. G. Wang, M. Chen, Z. Ding, J. Li, H. Yang, and P. Zhang, “Inter-patient ECG arrhythmia heartbeat classification based on unsupervised domain adaptation,” Neurocomputing, vol. 454, pp. 339–349, 2021.
  22. K. Weimann and T. O. Conrad, “Transfer learning for ECG classification,” Scientific reports, vol. 11, no. 1, pp. 1–12, 2021.
  23. Z. Lan, O. Sourina, L. Wang, R. Scherer, and G. R. Müller-Putz, “Domain adaptation techniques for EEG-based emotion recognition: A comparative study on two public datasets,” IEEE Transactions on Cognitive and Developmental Systems, vol. 11, no. 1, pp. 85–94, 2019.
  24. K. Yan, L. Kou, and D. Zhang, “Learning domain-invariant subspace using domain features and independence maximization,” IEEE transactions on cybernetics, vol. 48, no. 1, pp. 288–299, 2017.
  25. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” IEEE transactions on neural networks, vol. 22, no. 2, pp. 199–210, 2010.
  26. B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, “Unsupervised visual domain adaptation using subspace alignment,” in Proceedings of the IEEE international conference on computer vision, 2013, pp. 2960–2967.
  27. H. Zhao, Q. Zheng, K. Ma, H. Li, and Y. Zheng, “Deep representation-based domain adaptation for nonstationary EEG classification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 2, pp. 535–545, 2021.
  28. I. Misra and L. v. d. Maaten, “Self-supervised learning of pretext-invariant representations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 6707–6717.
  29. T. Mehari and N. Strodthoff, “Self-supervised representation learning from 12-lead ECG data,” Computers in Biology and Medicine, vol. 141, p. 105114, 2022.
  30. A. Ballas, V. Papapanagiotou, A. Delopoulos, and C. Diou, “Listen to your heart: A self-supervised approach for detecting murmur in heart-beat sounds,” in 2022 Computing in Cardiology (CinC), vol. 49.   IEEE, 2022.
  31. P. Sarkar and A. Etemad, “Self-supervised ECG representation learning for emotion recognition,” IEEE Transactions on Affective Computing, 2020.
  32. 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, pp. 1–6.
  33. H. Banville, O. Chehab, A. Hyvärinen, D.-A. Engemann, and A. Gramfort, “Uncovering the structure of clinical EEG signals with self-supervised learning,” Journal of Neural Engineering, vol. 18, no. 4, p. 046020, 2021.
  34. A. Gramfort, H. Banville, O. Chehab, A. Hyvärinen, and D. Engemann, “Learning with self-supervision on eeg data,” in 2021 9th International Winter Conference on Brain-Computer Interface (BCI), 2021, pp. 1–2.
  35. A. Natarajan et al., “A wide and deep transformer neural network for 12-lead ECG classification,” in 2020 Computing in Cardiology, 2020, pp. 1–4.
  36. J. Sun, J. Xie, and H. Zhou, “EEG classification with transformer-based models,” in 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), 2021, pp. 92–93.
  37. Z. Wang, Y. Wang, C. Hu, Z. Yin, and Y. Song, “Transformers for EEG-based emotion recognition: A hierarchical spatial information learning model,” IEEE Sensors Journal, vol. 22, no. 5, pp. 4359–4368, 2022.
  38. T. Dissanayake, T. Fernando, S. Denman, H. Ghaemmaghami, S. Sridharan, and C. Fookes, “Domain generalization in biosignal classification,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 6, pp. 1978–1989, 2021.
  39. H. Hasani, A. Bitarafan, and M. S. Baghshah, “Classification of 12-lead ECG signals with adversarial multi-source domain generalization,” in 2020 Computing in Cardiology, 2020, pp. 1–4.
  40. Y. Ganin et al., “Domain-adversarial training of neural networks,” The journal of machine learning research, vol. 17, no. 1, pp. 2096–2030, 2016.
  41. B.-Q. Ma, H. Li, W.-L. Zheng, and B.-L. Lu, “Reducing the subject variability of EEG signals with adversarial domain generalization,” in International Conference on Neural Information Processing.   Springer, 2019, pp. 30–42.
  42. B. Hariharan, P. Arbelaez, R. Girshick, and J. Malik, “Hypercolumns for object segmentation and fine-grained localization,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).   Boston, MA, USA: IEEE, Jun. 2015, pp. 447–456.
  43. M. Toğaçar, Z. Cömert, and B. Ergen, “Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer’s disease stages by deep learning model,” Neural Comput & Applic, vol. 33, no. 16, pp. 9877–9889, Aug. 2021.
  44. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).   Honolulu, HI: IEEE, Jul. 2017, pp. 2261–2269. [Online]. Available: https://ieeexplore.ieee.org/document/8099726/
  45. A. Ballas and C. Diou, “Cnns with multi-level attention for domain generalization,” ser. ICMR ’23.   New York, NY, USA: Association for Computing Machinery, 2023.
  46. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, ser. Lecture Notes in Computer Science, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds.   Cham: Springer International Publishing, 2015, pp. 234–241.
  47. M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz, “Invariant risk minimization,” arXiv:1907.02893 [cs, stat], Mar. 2020, arXiv: 1907.02893.
  48. X. Zhang, P. Cui, R. Xu, L. Zhou, Y. He, and Z. Shen, “Deep stable learning for out-of-distribution generalization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
  49. F. M. Carlucci, A. D’Innocente, S. Bucci, B. Caputo, and T. Tommasi, “Domain generalization by solving jigsaw puzzles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
  50. H. Nam, H. Lee, J. Park, W. Yoon, and D. Yoo, “Reducing domain gap by reducing style bias,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 8690–8699.
  51. D. Li, Y. Yang, Y.-Z. Song, and T. M. Hospedales, “Learning to generalize: Meta-learning for domain generalization,” in Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
  52. C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proceedings of the 34th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research.   PMLR, 2017.
  53. Y. Du et al., “Learning to learn with variational information bottleneck for domain generalization,” in Computer Vision – ECCV 2020.   Cham: Springer International Publishing, 2020.
  54. Z. Huang, H. Wang, E. P. Xing, and D. Huang, “Self-challenging improves cross-domain generalization,” in ECCV, 2020.
  55. S. Seo, Y. Suh, D. Kim, G. Kim, J. Han, and B. Han, “Learning to optimize domain specific normalization for domain generalization,” in Computer Vision – ECCV 2020.   Cham: Springer International Publishing, 2020.
  56. B. Venkatesh, J. J. Thiagarajan, K. Thopalli, and P. Sattigeri, “Calibrate and prune: Improving reliability of lottery tickets through prediction calibration,” 2020.
  57. H. Li, S. J. Pan, S. Wang, and A. C. Kot, “Domain generalization with adversarial feature learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 5400–5409.
  58. B. Sun, J. Feng, and K. Saenko, “Return of frustratingly easy domain adaptation,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1, 2016.
  59. B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in European conference on computer vision.   Springer, 2016, pp. 443–450.
  60. V. Vapnik, “Principles of risk minimization for learning theory,” Advances in neural information processing systems, vol. 4, 1991.
  61. I. Gulrajani and D. Lopez-Paz, “In search of lost domain generalization,” in International Conference on Learning Representations, 2021.
  62. A. Paszke et al., “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds.   Curran Associates, Inc., 2019, pp. 8024–8035.
  63. F. Liu et al., “An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection,” Journal of Medical Imaging and Health Informatics, vol. 8, no. 7, pp. 1368–1373, Sep. 2018.
  64. V. Tihonenko, A. Khaustov, S. Ivanov, and A. Rivin, “St.-Petersburg institute of cardiological technics 12-lead arrhythmia database,” 2007, type: dataset.
  65. P. Wagner et al., “PTB-XL, a large publicly available electrocardiography dataset,” Scientific Data, vol. 7, no. 1, p. 154, May 2020, number: 1 Publisher: Nature Publishing Group.
  66. T. Song, W. Zheng, P. Song, and Z. Cui, “EEG emotion recognition using dynamical graph convolutional neural networks,” IEEE Transactions on Affective Computing, vol. 11, no. 3, pp. 532–541, 2018.
  67. L. v. d. Maaten and G. Hinton, “Visualizing Data using t-SNE,” Journal of Machine Learning Research, vol. 9, no. 86, pp. 2579–2605, 2008.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Aristotelis Ballas (11 papers)
  2. Christos Diou (40 papers)
Citations (9)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now