中国邮电高校学报(英文版) ›› 2022, Vol. 29 ›› Issue (6): 83-96.doi: 10.19682/j.cnki.1005-8885.2022.1008
• Others • 上一篇
Jiang Fan, Chen Jiajun, Gao Youjun, Sun Changyin
Jiang Fan, Chen Jiajun, Gao Youjun, Sun Changyin
Concerning current deep learning-based electrocardiograph ( ECG) classification methods, there exists domain discrepancy between the data distributions of the training set and the test set in the inter-patient paradigm. To reduce the negative effect of domain discrepancy on the classification accuracy of ECG signals, this paper incorporates transfer learning into the ECG classification, which aims at applying the knowledge learned from the raining set to the test set. Specifically, this paper first develops a deep domain adaptation network ( DAN) for ECG classification based on the convolutional neural network ( CNN). Then, the network is pre-trained with training set data obtained from the famous Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) ECG arrhythmia database. On this basis, by minimizing the multi-kernel maximum mean discrepancy (MK-MMD) between the data distributions of the training set and the test set, the pre-trained network is adjusted to learn transferable feature representations. Finally, with the low-density separation of unlabeled target data, the feature representations are more transferable. The extensive experimental results show that the proposed domain adaptation method has reached a 7. 58% improvement in overall classification accuracy on the test set, and achieves competitive performance with other state-of-the-arts.
1. ROTH G A, MENSAH G A, JOHNSON C O, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: Update from the GBD 2019 study. Journal of the American College of Cardiology, 2020, 76(25): 2982-3021.
2. ALIM A, ISLAM K. Application of machine learning on ECG signal classification using morphological features. Proceedings of the 2020 IEEE Region 10 Symposium (TENSYMP’20), 2020, Jun 5-7, Dhaka, Bangladesh. Piscataway, NJ, USA: IEEE, 2020: 1632-1635.
3. INCE T, KIRANYAZ S, GABBOUJ M. A generic and robust system for automated patient-specific classification of ECG signals. IEEE Transactions on Biomedical Engineering, 2009, 56(5): 1415-1426.
4. GE Z Y, ZHU Z H, FENG P P, et al. ECG-signal classification using SVM with multi-feature. Proceedings of the 8th International Symposium on Next Generation Electronics (ISNE’19), 2019, Oct 9-10, Zhengzhou, China. Piscataway, NJ, USA: IEEE, 2019: 3p.
5. SUBRAMANIAN K, PRAKASH N K. Machine learning based cardiac arrhythmia detection from ECG signal. Proceedings of the 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT’20), 2020, Aug 20-22, Tirunelveli, India. Piscataway, NJ, USA: IEEE, 2020: 1137-1141.
6. PRAKASH A J, ARI S. AAMI standard cardiac arrhythmia detection with random forest using mixed features. Proceedings of the IEEE 16th India Council International Conference (INDICON’19), 2019, Dec 13-15, Rajkot, India. Piscataway, NJ, USA: IEEE, 2019: 4p.
7. BAO J C. Multi-features based arrhythmia diagnosis algorithm using XGBoost. Proceedings of the 2020 International Conference on Computing and Data Science (CDS’20), 2020, Aug 1-2, Stanford, CA, USA. Piscataway, NJ, USA: IEEE, 2020: 454-457.
8. LIU Z D, MENG X A, CUI J J, et al. Automatic identification of abnormalities in 12-lead ECGs using expert features and convolutional neural networks. Proceedings of the 2018 International Conference on Sensor Networks and Signal Processing (SNSP’18), 2018, Oct 28-31, Xi'an, China. Piscataway, NJ, USA: IEEE, 2018: 163-167.
9. HUANG J S, CHEN B Q, YAO B, et al. ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network. IEEE Access, 2019, 7: 92871-92880.
10. WANG E K, ZHANG X, PAN L Y. Automatic classification of CAD ECG signals with SDAE and bidirectional long short-term network. IEEE Access, 2019, 7: 182873-182880
11. XIONG Z H, STILES M K, ZHAO J C. Robust ECG signal classification for detection of atrial fibrillation using a novel neural network. Proceedings of the 2017 Computing in Cardiology (CinC’17), 2017, Sept 24-27, Rennes, France. Piscataway, NJ, USA: IEEE, 2017: 4p
12. OQUAB M, BOTTOU L, LAPTEV I, et al. Learning and transferring mid-level image representations using convolutional neural networks. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, Jun 23-28, Columbus, OH, USA. Piscataway, NJ, USA: IEEE, 2014: 1717-1724
13. PAN S J, YANG Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359
14. DIKER A, CÖMERT Z, AVCI E, et al. A novel application based on spectrogram and convolutional neural network for ECG classification. Proceedings of the 1st International Informatics and Software Engineering Conference (UBMYK’19), 2019, Nov 6-7, Ankara, Turkey. Piscataway, NJ, USA: IEEE, 2019: 6p
15. TADESSE G A, ZHU T T, LIU Y, et al. Cardiovascular disease diagnosis using cross-domain transfer learning. Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’19), 2019, Jul 23-27, Berlin, Germany. Piscataway, NJ, USA: IEEE, 2019: 4262-4265
16. ALMALCHY M T, ALGAYAR S M S, POPESCU N. Atrial fibrillation automatic diagnosis based on ECG signal using pretrained deep convolution neural network and SVM multiclass model. Proceedings of the 13th International Conference on Communications (COMM’20), 2020, Jun 18-20, Bucharest, Romania. Piscataway, NJ, USA: IEEE, 2020: 197-202
17. QUIONERO-CANDELA J, SUGIYAMA M, SCHWAIGHOFER A, et al. Dataset shift in machine learning. Cambridge, MA, USA: MIT Press, 2009
18. TZENG E, HOFFMAN J, ZHANG N, et al. Deep domain confusion: Maximizing for domain invariance. arXiv Preprint, arXiv:1412.3474, 2014
19. LONG M S, CAO Y, CAO Z J, et al. Transferable representation learning with deep adaptation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(12): 3071-3085
20. YOSINSKI J, CLUNE J, BENGIO Y, et al. How transferable are features in deep neural networks？Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14): Vol 2, 2014, Dec 8-13, Montreal, Canada. Cambridge, MA, USA: MIT Press, 2014: 3320-3328
21. GRETTON A, BORGWARDT K M, RASCH M J, et al. A kernel two-sample test. Journal of Machine Learning Research, 2012, 13(25): 723-773
22. GRETTON A, SRIPERUMBUDUR B, SEJDINOVIC D, et al. Optimal kernel choice for large-scale two-sample tests. Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS’12): Vol 1, 2012, Dec 3-6, Lake Tahoe, NV, USA. Red Hook, NY, USA: Curran Associates Inc, 2012: 1205-1213
23. GRANDVALET Y, BENGIO Y. Semi-supervised learning by entropy minimization. Advances in Neural Information Processing Systems : Proceedings of the 17th International Conference on Neural Information Processing Systems (NIPS’04), 2004, Dec 13-18, Vancouver, Canada. Cambridge, MA, USA: MIT Press, 2004: 529-536
24. DE CHAZAL P, O'DWYER M, REILLY R B, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 2004, 51(7): 1196-1206
25. AAMI. Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. AAMI EC57:2012. Arlington, VA, USA: AAMI, 1998
26. SELLAMI A, HWANG H. A robust deep convolutional neural network with batch-weighted loss for heartbeat classification. Expert Systems with Applications, 2019, 122: 75-84
27. ZHANG P, CHENG J Y, ZHAO Y X. Classification of ECG signals based on LSTM and CNN.Artificial Intelligence and Security: Proceedings of the 6th International Conference on Artificial Intelligence and Security (ICAIS’20): Part III, 2020, Jul 17-20, Hohhot, China. CCIS 1254. Berlin, Germany: Springer, 2020: 278-289
28. NIU L S, CHEN C, LIU H, et al. A deep-learning approach to ECG classification based on adversarial domain adaptation. Healthcare, 2020, 8(4): 437-452
29. AMMOUR N. Atrial fibrillation detection with a domain adaptation neural network approach. Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence (CSCI’18), 2018, Dec 12-14, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE, 2018: 738-743
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