AUTOMATIC CLASSIFICATION OF ECG USING DEEP LEARNING MODEL

Hoàng Văn Mạnh; Đỗ Nam; Phạm Mạnh Thắng

doi:10.34238/tnu-jst.8628

AUTOMATIC CLASSIFICATION OF ECG USING DEEP LEARNING MODEL

About this article

Received: 24/08/23 Revised: 17/10/23 Published: 17/10/23

Authors

1. Hoang Van Manh , University of Engineering and Technology - Vietnam National University, Hanoi
2. Do Nam, University of Engineering and Technology - Vietnam National University, Hanoi
3. Pham Manh Thang, University of Engineering and Technology - Vietnam National University, Hanoi

Abstract

Early detection of cardiovascular diseases through ECG signals has played an important role in the treatment process for patients. The application of artificial intelligence to develop an automatic method of classifying ECG signals with high accuracy and reliability to help reduce the time of diagnosis is an inevitable requirement. This study proposes a deep learning model that combines a Densely Connected Convolutional Networks (DenseNet) with a Bidirectional Long Short-Term Memory Networks (BiLSTM) with a small number of parameters into the ECG signal classification. The proposed model is evaluated on an open database consisting of 827 ECG records. Although the study only uses a small amount of data for the training process, the proposed model still gives good results, in particularly, F1 scores corresponding to the types of right bundle branch block, left bundle branch block and sinus bradycardia are 0.831, 0.846 and 0.882 respectively. The obtained results can serve as a basis for further studies applying on devices with limited resources.

Keywords

ECG; DenseNet; BiLSTM; Deep learning; Artificial intelligence

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References

[1] D. S. Tawfik, J. Profit, T. I. Morgenthaler, et al., "Physician burnout, well-being, and work unit safety grades in relationship to reported medical errors," Mayo Clinic Proceedings, vol. 93, no. 11, pp. 1571–1580, 2018, doi: 10.1016/j.mayocp.2018.05.014.

[2] C. P. West, M. M. Huschka, P. J. Novotny, et al., "Association of perceived medical errors with resident distress and empathy: a prospective longitudinal study," Jama, vol. 296, no. 9, pp. 1071–1078, 2006, doi: 10.1001/jama.296.9.1071.

[3] G. D. Clifford, C. Liu, B. Moody, et al., "AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017," in Proc. Computing in Cardiology (CinC), Rennes, France, 2017, pp. 1–4, doi: 10.22489/CinC.2017.065-469.

[4] T. Teijeiro, C. A. García, D. Castro, et al., "Arrhythmia classification from the abductive interpretation of short single-lead ECG records," in Proc. Computing in Cardiology (CinC), Rennes, France, 2017, pp. 1–4, doi: 10.22489/CinC.2017.166-054.

[5] M. Zabihi, A. B. Rad, A. K. Katsaggelos, et al., "Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier," in Proc. Computing in Cardiology (CinC), Rennes, France, 2017, pp. 1–4, doi: 10.22489/CinC.2017.069-336.

[6] S. Datta, C. Puri, A. Mukherjee, et al., "Identifying normal, AF and other abnormal ECG rhythms using a cascaded binary classifier," in Proc. Computing in Cardiology (CinC), Rennes, France, 2017, pp. 1–4, doi: 10.22489/CinC.2017.173-154.

[7] S. Hong, M. Wu, Y. Zhou, et al., "ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks," in Proc. Computing in Cardiology (CinC), Rennes, France, 2017, pp. 1–4, doi: 10.22489/CinC.2017.178-245.

[8] F. Liu, C. Liu, L. Zhao, 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, 2018, doi:10.1166/jmihi.2018.2442.

[9] T. M. Chen, C. H. Huang, E. S. Shih, et al., "Detection and classification of cardiac arrhythmias by a challenge-best deep learning neural network model," Iscience, vol. 23, no. 3, pp. 1–7, 2020, doi: 10.1016/j.isci.2020.100886.

[10] G. K. Andrikopoulos, P. E. Dilaveris, D. J. Richter, et al., "Increased variance of P wave duration on the electrocardiogram distinguishes patients with idiopathic paroxysmal atrial fibrillation," Pacing and Clinical Electrophysiology, vol. 23, no. 7, pp. 1127–1132, 2000, doi: 10.1111/j.1540-8159.2000.tb00913.x.

[11] K. Aytemir, N. ÖZER, E. Atalar, et al., "P wave dispersion on 12‐lead electrocardiography in patients with paroxysmal atrial fibrillation," Pacing and Clinical Electrophysiology, vol. 23, no. 7, pp. 1109–1112, 2000, doi: 10.1111/j.1540-8159.2000.tb00910.x.

[12] S. Ladavich and B. Ghoraani, "Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity," Biomedical Signal Processing and Control, vol. 18, pp. 274–281, 2015, doi: 10.1016/j.bspc.2015.01.007.

[13] J. Lian, L. Wang, and D. Muessig, "A simple method to detect atrial fibrillation using RR intervals," The American journal of cardiology, vol. 107, no. 10, pp. 1494–1497, 2011, doi: 10.1016/j.amjcard.2011.01.028.

[14] C. Huang, S. Ye, H. Chen, et al., "A novel method for detection of the transition between atrial fibrillation and sinus rhythm," IEEE Transactions on Biomedical Engineering, vol. 58, no. 4, pp. 1113–1119, 2010, doi: 10.1109/TBME.2010.2096506.

[15] R. Couceiro, P. Carvalho, J. Henriques, et al., "Detection of atrial fibrillation using model-based ECG analysis," in Proc. IEEE 19th Int. Conf. on pattern recognition, pp. 1–5, 2008, doi: 10.1109/ICPR.2008.4761755.

[16] S. Babaeizadeh, R. E. Gregg, E. D. Helfenbein, et al., "Improvements in atrial fibrillation detection for real-time monitoring," Journal of electrocardiology, vol. 42, no. 6, pp. 522–526, 2009, doi: 10.1016/j.jelectrocard.2009.06.006.

[17] S. Asgari, A. Mehrnia, and M. Moussavi, "Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine," Computers in biology and medicine, vol. 60, pp. 132–142, 2015, doi: 10.1016/j.compbiomed.2015.03.005.

[18] X. Xu, S. Wei, C. Ma, et al., "Atrial fibrillation beat identification using the combination of modified frequency slice wavelet transform and convolutional neural networks," Journal of Healthcare Engineering, pp. 1–8, 2018, doi: 10.1155/2018/2102918.

[19] Y. Xia, N. Wulan, K. Wang, et al., "Detecting atrial fibrillation by deep convolutional neural networks," Computers in Biology and Medicine, vol. 93, pp. 84-92, 2018, doi: 10.1016/j.compbiomed.2017.12.007.

[20] R. Salloum and C. C. J. Kuo, "ECG-based biometrics using recurrent neural networks," in Proc. IEEE Inter. Conf. Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017, pp. 2062-2066, doi: 10.1109/ICASSP.2017.7952519.

[21] M. Kachuee, S. Fazeli, and M. Sarrafzadeh, "ECG Heartbeat Classification: A Deep Transferable Representation," in Proc. IEEE Inter. Conf. healthcare informatics (ICHI), New York, NY, USA, 2018, pp. 443-444, doi: 10.1109/ICHI.2018.00092.

[22] K. Weimann and T. O. Conrad, "Transfer learning for ECG classification," Scientific reports, vol. 11, no. 1, 2021, doi: 10.1038/s41598-021-84374-8.

[23] N. Strodthoff, P. Wagner, T. Schaeffter, et al., "Deep learning for ECG analysis: Benchmarks and insights from PTB-XL," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 5, pp. 1519-1528, 2020, doi: 10.1109/JBHI.2020.3022989.

[24] A. Mostayed, J. Luo, X. Shu, et al., "Classification of 12-Lead ECG Signals with Bi-Directional LSTM Network,” 2018. Online. Available: http://arxiv.org/abs/1811.02090. [Accessed July 15, 2023].

[25] J.R.G. de Santana, M.G.F. Costa, and C.F.F.C. Filho, "A New Approach to Classify Cardiac Arrythmias Using 2D Convolutional Neural Networks," in Proc. IEEE 43^rd Annu. Int. Conf. IEEE Eng. Med. Biol. (EMBC), 2021, pp. 566-570, doi: 10.1109/EMBC46164.2021.9630938.

[26] M. B. Alkmim, R. M. Figueira, M. S. Marcolino, et al., "Improving patient access to specialized health care: the Telehealth Network of Minas Gerais, Brazil," Bulletin of the World Health Organization, vol. 90, pp. 373-378, 2012, doi: 10.2471/BLT.11.099408.

[27] G.Huang, Z. Liu, L. V. D. Maaten, et al., "Densely connected convolutional networks," in Proc. IEEE Conf. computer vision and pattern recognition (CVPR), Honolulu, HI, USA, 2017, pp. 2261-2269, doi: 10.1109/CVPR.2017.243.

[28] Z. Yu, V. Ramanarayanan, D. Suendermann-Oeft, et al., "Using bidirectional LSTM recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech, " in 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Scottsdale, AZ, USA, 2015, pp. 338-345, doi: 10.1109/ASRU.2015.7404814.

[29] Z. C. Lipton, J. Berkowitz, and C. Elkan, "A critical review of recurrent neural networks for sequence learning," arXiv preprint arXiv:1506.00019, doi: 10.48550/arXiv.1506.00019.

DOI: https://doi.org/10.34238/tnu-jst.8628

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