Deteksi Penyakit Kardiovaskular pada Isyarat EKG Berbasis Deep Learning
Abstract
The purpose of this study is to review studies related to the CNN method in predicting cardiovascular disease so that it can explain the comparison of results between the CNN studies to detect cardiovascular disease that have been conducted. The research method used is a literature review. The results of the study show that the use of the CNN method to detect cardiovascular disease has excellent results, with the highest percentage of accuracy reaching 99.79%, the highest percentage of F1-score achieving 99.78%, the highest percentage of specificity achieving 98.35%, and the highest sensitivity reaching 99.71%. In conclusion, the CNN method can be implemented into a clinical decision support system that health workers such as doctors and nurses can use to assess patient health. Further research must consider the proposed CNN architectural model using the database to outperform existing research.
Keywords: Convolutional Neural Network, Electrocardiogram, Detection of Cardiovascular Disease
References
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World Health Organization. (2021). Cardiovascular Diseases. WHO. https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1
Avanzato, R., & Beritelli, F. (2020). Automatic ECG Diagnosis Using Convolutional Neural Network. Electronics, 9(6), 1-14. https://doi.org/10.3390/electronics9060951
Banerjee, R., Ghose, A., & Mandana, D. K. M. (2020). A Hybrid CNN-LSTM Architecture for Detection of Coronary Artery Disease from ECG. IEEE, 1-8. https://ieeexplore.ieee.org/document/9207044
Chen, Y., Nasrawi, D., Massey, D., Johnston, A. N. B., Keller, K., & Kunst, E. (2021). Final-Year Nursing Students' Foundational Knowledge and Self-Assessed Confidence in Interpreting Cardiac Arrhythmias: A Cross-Sectional Study. Nurse Education Today, 97, 104699. https://doi.org/10.1016/j.nedt.2020.104699
Fikri, M. R., Soesanti, I., & Nugroho, H. A. (2021). ECG Signal Classification Review. IJITEE (International Journal of Information Technology and Electrical Engineering), 5(1), 15-20. https://jurnal.ugm.ac.id/ijitee/article/download/60295/31492
Hasan, N. I., & Bhattacharjee, A. (2019). Deep Learning Approach to Cardiovascular Disease Classification Employing Modified ECG Signal from Empirical Mode Decomposition. Biomedical Signal Processing and Control, 52, 128–140. https://doi.org/10.1016/j.bspc.2019.04.005
Ho, J. K., Yau, C. H., Wong, C. Y., & Tsui, J. S. (2021). Capability of Emergency Nurses for Electrocardiogram Interpretation. International Emergency Nursing, 54, 100953. https://doi.org/10.1016/j.ienj.2020.100953
Huang, J., Chen, B., Yao, B., & He, W. (2019). ECG Arrhythmia Classification Using STFT-Based Spectrograms and Neural Networks Convolutions. IEEE, 7. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8759878
Kalluri, H. K., & Krishna, S. T. (2020). A Deep Learning Method for Prediction of Cardiovascular Disease Using Convolutional Neural Network. International Information and Engineering Technology Association, 34(5), 601-606. http://dx.doi.org/10.18280/ria.340510
Kusuma, S., & Udayan, J. D. (2020). Analysis on Deep Learning Methods for ECG Based Cardiovascular Disease Prediction. Scalable Computing, 21(1), 127-136. https://doi.org/10.12694/scpe.v21i1.1640
Li, J., Si, Y., Xu, T., & Jiang, S. (2018). Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques. Hindawi, 2018, 1-10. https://doi.org/10.1155/2018/7354081
Maharani, A., Sujarwoto, S., Praveen, D., Oceandy, D., Tampubolon, G., & Patel, A. (2019). Cardiovascular Disease Risk Factor Prevalence and Estimated 10-Year Cardiovascular Risk Scores in Indonesia: The SMARThealth Extend Study. PloS One, 14(4), e0215219. https://doi.org/10.1371/journal.pone.0215219
Mehmood, A., Iqbal, M., Mehmood, Z., Irtaza, A., Nawaz, M., Nazir, T., & Masood, M. (2020). Prediction of Heart Disease Using Deep Convolutional Neural Networks. Arabian Journal for Science and Engineering, 46, 3409–3422. https://doi.org/10.1007/s13369-020-05105-1
Namara, K. M., Alzubaidi, H., & Jackson, J. K. (2019). Cardiovascular Disease as a Leading Cause of Death: How Are Pharmacists Getting Involved?. Integrated Pharmacy Research & Practice, 8, 1–11. https://doi.org/10.2147/IPRP.S133088
Pan, Y., Fu, M., Cheng, B., Tao, X., & Guo, J. (2020). Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction on the Internet of Medical Things Platform. IEEE, 8, 189503-189512. doi: 10.1109/ACCESS.2020.3026214
Penalo, L., Pusic, M., Friedman, J. L., Rosenzweig, B. P., & Lorin, J. D. (2021). Importance Ranking of Electrocardiogram Rhythms: A Primer for Curriculum Development. Journal of Emergency Nursing, 47(2), 313–320. https://doi.org/10.1016/j.jen.2020.11.005
Tahboub, O. Y., & Yılmaz, Ü. D. (2019). Nurses’ Knowledge and Practices of Electrocardiogram Interpretation. International Cardiovascular Research Journal, 13(3), 80-84. https://brieflands.com/articles/ircrj-91025.html
World Health Organization. (2021). Cardiovascular Diseases. WHO. https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1
