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http://dx.doi.org/10.6109/jkiice.2022.26.1.76

Arrhythmia Classification using Hybrid Combination Model of CNN-LSTM  

Cho, Ik-Sung (School of Interdisciplinary Studies, Daegu University,)
Kwon, Hyeog-Soong (Department of IT Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.1, 2022 , pp. 76-84 More about this Journal
Abstract
Arrhythmia is a condition in which the heart beats abnormally or irregularly, early detection is very important because it can cause dangerous situations such as fainting or sudden cardiac death. However, performance degradation occurs due to personalized differences in ECG signals. In this paper, we propose arrhythmia classification using hybrid combination model of CNN-LSTM. For this purpose, the R wave is detected from noise removed signal and a single bit segment was extracted. It consisted of eight convolutional layers to extract the features of the arrhythmia in detail, used them as the input of the LSTM. The weights were learned through deep learning and the model was evaluated by the verification data. The performance was compared in terms of the accuracy, precision, recall, F1 score through MIT-BIH arrhythmia database. The achieved scores indicate 92.3%, 90.98%, 92.20%, 90.72% in terms of the accuracy, precision, recall, F1 score, respectively.
Keywords
Arrhythmia; CNN; LSTM; Deep learning; MIT-BIH;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 S. Singh, S. K. Pandey, U. Pawar, and R. R. Janghel, "Classification of ECG Arrhythmia using Recurrent Neural Networks," Procedia Computer Science, vol. 132, pp. 1290-1297, Jun. 2018.   DOI
2 H. M. Tun, W. K. Moe, and Z. M. Naing, "Analysis on conversion process from paper record ECG to computer based ECG," MedCrave Online Journal of Applied Bionics and Biomechanics, vol. 1, no. 2, pp. 69-81, Sep. 2017.
3 A. Hossain, R. Quaresma, and H. Rahman, "Investigating factors influencing the physicians' adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study," International Journal of Information Management, vol. 44, pp. 76-87, Feb. 2019.   DOI
4 M. J. Goldman, Principles of Clinical Electrocardiography, Los Altos California: Lange Medical Pubns, 1986.
5 K. Hanbay, "Deep neural network based approach for ECG classification using hybrid differential features and active learning," Institution of Engineering and Technology, vol. 13, no. 2, pp. 165-175, May. 2019.
6 W. Li, "Deep Intermediate Representation and In-Set Voting Scheme for Multiple-Beat Electrocardiogram Classification," IEEE Sensors Journal, vol. 19, no. 16, pp. 6895-6904, Apr. 2019.   DOI
7 M. Altuve and F. Hernandez, "Multiclass Classification of Cardiac Rhythms on Short Single Lead ECG Recordings using Bidirectional Long Short-Term Memory Networks," IEEE Latin America Transactions, vol. 19, no. 7, pp. 1207-1216, Jul. 2021.   DOI
8 S. Xu, J. Li, K. Liu, and L. Wu, "A Parallel GRU Recurrent Network Model and its Application to Multi-Channel Time-Varying Signal Classification," IEEE Access, vol. 7, pp. 118739-118748, Aug. 2019.   DOI
9 I. S. Cho and H. S. Kwon, "Optimal Threshold Setting Method for R Wave Detection According to The Sampling Frequency of ECG Signals," Journal of Korea Institute of Information and Communication Engineering, vol. 21, no. 7, pp. 1420-1428, Jul. 2017.   DOI
10 Y. Wei, J. Zhou, Y. Wang, Y. Liu, Q. Liu, J. Luo, C. Wang, F Ren, and L. Huang, "A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications," IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 2, pp. 145-163, Apr. 2020.   DOI
11 Y. Li, Z. Xia, and Y. Zhang, "Standalone Systolic Profile Detection of Non-Contact SCG Signal With LSTM Network," IEEE Sensors Journal, vol. 20, no. 6, pp. 3123-3131, Mar. 2020.   DOI
12 Q. Qin, J. Li, Y. Yue, and C. Liu, "An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm," Journal of Healthcare Engineering, vol. 2017, pp. 1-14, Sep. 2017.
13 R. He, Y. Liu, K. Wang, N. Zhao, Y. Yuan, Q. Li, and H. Zhang, "Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM," IEEE Access, vol. 7, pp. 102119-102135, Jul. 2019.   DOI
14 A. Sellami and H. Hwang, "A robust deep convolutional neural network with batch-weighted loss for heartbeat classification," Expert Systems with Applications, vol. 122, pp. 75-84, May. 2019.   DOI