Deep Learning and Its Role in Diagnosing Heart Diseases Based on Electrocardiography (ECG)
DOI:
https://doi.org/10.37385/jaets.v5i2.3746Keywords:
Diagnosing, Heart, CNN, SignalAbstract
Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was adopted, which is considered a challenge for researchers because it includes different age groups. Many diseases, and the results obtained by the system were 96% accurate.
Downloads
References
Abd El-Rahiem, B., & Hammad, M. (2022). A Multi-fusion IoT Authentication System Based on Internal Deep Fusion of ECG Signals (Issue October). Springer International Publishing. https://doi.org/10.1007/978-3-030-85428-7_4
Abdelghani, S. A., Rosenthal, T. M., & Morin, D. P. (2016). Surface electrocardiogram predictors of sudden cardiac arrest. Ochsner Journal, 16(3), 280–289. https://doi.org/ 10.1161/CIRCULATIONAHA.112.128413
Abed, R. M., Abdulmalek, H. W., Yaaqoob, L. A., Altaee, M. F., & Kamona, Z. K. (2023). Genetic Polymorphism of TLR5 and TLP6 in Iraqi Patients with Heart Failure Disease. Iraqi Journal of Science, 1662–1674. https://doi.org/ 10.24996/ijs.2023.64.4.9
Abood, Q. K. (2023). Predicting Age and Gender Using AlexNet. TEM Journal, 12(1). https://doi.org/ 10.18421/TEM121-61
AL-Jibory, F. K. (2021). Hybrid System for Plagiarism Detection on A Scientific Paper. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 5707–5719. https://doi.org/10.17762/turcomat.v12i13.9822
Al-Juboori, R. A. L. (2017). Contrast enhancement of the mammographic image using retinex with CLAHE methods. Iraqi Journal of Science, 327–336.
Alnaggar, M., Handosa, M., Medhat, T., & Rashad, M. Z. (2023). An IoT-based Framework for Detecting Heart Conditions using Machine Learning. International Journal of Advanced Computer Science and Applications, 14(4). https://doi.org/ 10.14569/IJACSA.2023.0140442
Alqahtani, N., Alam, S., Aqeel, I., Shuaib, M., Mohsen Khormi, I., Khan, S. B., & Malibari, A. A. (2023). Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification. Applied Sciences, 13(13), 7833. https://doi.org/10.3390/app13137833
Bhanjaa, M. N., & Khampariya, P. (2023). Design and Comparison of Deep Learning Model for ECG Classification using PTB-XL Dataset. https://doi.org/ https://doi.org/10.3390/e23091121
Borghi de Melo, P. H. (2021). Brief Review on Electrocardiogram Analysis and Classification Techniques with Machine Learning Approaches. U.Porto Journal of Engineering, 7(4), 153–162. https://doi.org/10.24840/2183-6493_007.004_0012
El Boujnouni, I., Harouchi, B., Tali, A., Rachafi, S., & Laaziz, Y. (2023). Automatic diagnosis of cardiovascular diseases using wavelet feature extraction and convolutional capsule network. Biomedical Signal Processing and Control, 81, 104497. https://doi.org/ 10.1016/j.bspc.2022.104497
Gimeno-Blanes, F. J., Blanco-Velasco, M., Barquero-Pérez, Ó., García-Alberola, A., & Rojo-álvarez, J. L. (2016). Sudden cardiac risk stratification with electrocardiographic indices - A review on computational processing, technology transfer, and scientific evidence. Frontiers in Physiology, 7(MAR), 1–17. https://doi.org/10.3389/fphys.2016.00082
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. https://doi.org/10.1038/s41591-018-0268-3. https://doi.org/10.1038/s41591-018-0268-3
Harmon, D. M., Attia, Z. I., & Friedman, P. A. (2022). Current and future implications of the artificial intelligence electrocardiogram: the transformation of healthcare and attendant research opportunities. Oxford University Press. https://doi.org/10.1093/cvr/cvac006
Ibrahim, A. E., Abdel-mageid, S., Arabia, S., Nada, N., & Elshahed, M. A. (n.d.). Human Identification Using Electrocardiogram Signal as a Biometric Trait. 11(3), 1–17. https://doi.org/10.4018/IJSDA.287113. http://doi.org/10.4018/IJSDA.287113
T. Tabassum and M. Islam, "An approach of cardiac disease prediction by analyzing ECG signal," 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, Bangladesh, 2016, pp. 1-5, doi: 10.1109/CEEICT.2016.7873093.
Khanna, A., Selvaraj, P., Gupta, D., Sheikh, T. H., Pareek, P. K., & Shankar, V. (2023). Internet of things and deep learning enabled healthcare disease diagnosis using biomedical electrocardiogram signals. Expert Systems, 40(4), e12864. https://doi.org/10.1111/exsy.12864.
khiled AL-Jibory, F., Younis, M. A., & Al-Tamimi, M. S. H. (2022). Preparing of ECG Dataset for Biometric ID Identification with Creative Techniques. https://doi.org/10.18421/TEM114-10, November 2022.
Kim, E., Kim, J., Park, J., Ko, H., & Kyung, Y. (2023). TinyML-Based Classification in an ECG Monitoring Embedded System. Computers, Materials and Continua, 75(1), 1751–1764. https://doi.org/10.32604/cmc.2023.031663
Li, H., & Boulanger, P. (2020). A survey of heart anomaly detection using ambulatory electrocardiogram (ECG). Sensors, 20(5), 1461. https://doi.org/10.3390/s20051461
Liu, F., Liu, C., Zhao, L., Zhang, X., Wu, X., Xu, X., Liu, Y., Ma, C., Wei, S., He, Z., Li, J., & Yin Kwee, E. N. (2018). An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection. Journal of Medical Imaging and Health Informatics, 8(7), 1368–1373. https://doi.org/10.1166/jmihi.2018.2442
Lynn, H. M., Yeom, S., & Kim, P. (2018). ECG-based biometric human identification based on backpropagation neural network. Proceedings of the 2018 Research in Adaptive and Convergent Systems, RACS 2018, 1, 6–10. https://doi.org/10.1145/3264746.3264760
Malakouti, S. M. (2023). Heart disease classification based on ECG using machine learning models. Biomedical Signal Processing and Control, 84, 104796. https://doi.org/10.1016/j.bspc.2023.104796
Abdulla, L. A., & Al-Ani, M. S. (2020). A review study for electrocardiogram signal classification. UHD Journal of Science and Technology, 4(1), 103-117. https://doi.org/ 10.21928/uhdjst.v4n1y2020.pp103-117.
Mincholé, A., & Rodriguez, B. (2019). Artificial intelligence for the electrocardiogram. Nature Medicine, 25(1), 22–23. https://doi.org/10.1038/s41591-018-0306-1
Mohammed, M. K., & Essa, S. I. (2022). Evaluation of the Physical Parameters on Ischemic Heart Disease Patients Using Echocardiography. Iraqi Journal of Science, https://doi.org/ 3354–3358. 10.24996/ijs.2022.63.8.10.
Rahma, M. M., & Salman, A. D. (2022). Heart Disease Classification–Based on the Best Machine Learning Model. Iraqi Journal of Science, 3966–3976. https://doi.org/ 10.24996/ijs.2022.63.9.28.
Ram, R. S., Akilandeswari, J., & Kumar, M. V. (2023). HybDeepNet: a hybrid deep learning model for detecting cardiac arrhythmia from ECG signals. Information Technology and Control, 52(2), 433–444. https://doi.org/ doi: 10.5755/j01.itc.52.2.32993
Ramkumar, M., Ganesh Babu, C., Karthikeyani, S., Priyanka, G. S., & Sarath Kumar, R. (2021). Probabilistic Feature Extraction Techniques for Electrocardiogram Signal-A Review. IOP Conference Series: Materials Science and Engineering, 1084(1), 012024. https://doi.org/10.1088/1757-899x/1084/1/012024
Sadiq, A. T., & Mahmood, N. T. (2014). A hybrid estimation system for medical diagnosis using modified full Bayesian classifier and artificial bee colony. Iraqi Journal of Science, 55(3A), 1095–1107.
Saini, S. K., & Gupta, R. (2022). Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges. Artificial Intelligence Review, 55(2), 1519–1565. https://doi.org/
Sandau, K. E., Funk, M., Auerbach, A., Barsness, G. W., Blum, K., Cvach, M., Lampert, R., May, J. L., McDaniel, G. M., Perez, M. V., Sendelbach, S., Sommargren, C. E., & Wang, P. J. (2017). https://doi.org/ 10.1007/s10462-021-09999-7
Update to Practice Standards for Electrocardiographic Monitoring in Hospital Settings: A Scientific Statement From the American Heart Association. In Circulation (Vol. 136, Issue 19). https://doi.org/10.1161/CIR.0000000000000527
Sansone, M., Fusco, R., Pepino, A., & Sansone, C. (2013). Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: A review. Journal of Healthcare Engineering, 4(4), 465–504. https://doi.org/10.1260/2040-2295.4.4.465
Saranya, R., & Murugan, A. (2023). A systematic review of enabling blockchain in healthcare system: Analysis, current status, challenges and future direction. Materials Today: Proceedings, 80, 3010–3015. https://doi.org/ 10.1016/j.matpr.2021.07.105
Shahriari, Y., Fidler, R., Pelter, M. M., Bai, Y., Villaroman, A., & Hu, X. (2018). Electrocardiogram Signal Quality Assessment Based on Structural Image Similarity Metric. IEEE Transactions on Biomedical Engineering, 65(4), 748–753. https://doi.org/10.1109/TBME.2017.2717876
Shi J, Li Z, Liu W, Zhang H, Guo Q, Chang S, Wang H, He J, Huang Q. Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics. Bioengineering (Basel). 2023 May 18;10(5):607. doi: 10.3390/bioengineering10050607. PMID: 37237677; PMCID: PMC10215604.
Shkara, A. A., & Hussain, Y. (2018). Heartbeat Amplification and ECG Drawing from Video (Black and White or Colored Videos). Iraqi Journal of Science, 408–419. https://doi.org/ doi: 10.24996/ijs.2018.59.1B.21
Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021). Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nature Reviews Cardiology, 18(7), 465–478. https://doi.org/10.1172/JCI178251.
Terzi, M. B., & Arikan, O. (2023). Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram. Biomedical Engineering/Biomedizinische Technik, 0. doi: 10.1515/bmt-2022-0406. Print 2024 Feb 26.
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access, 6, 35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950.
Yang, X. L., Liu, G. Z., Tong, Y. H., Yan, H., Xu, Z., Chen, Q., Liu, X., Zhang, H. H., Wang, H. B., & Tan, S. H. (2015). The history, hotspots, and trends of electrocardiogram. Journal of Geriatric Cardiology, 12(4), 448–456. https://doi.org/10.11909/j.issn.1671-5411.2015.04.018
Zontone, P., Affanni, A., Bernardini, R., Piras, A., & Rinaldo, R. (2019). Stress detection through electrodermal activity (EDA) and electrocardiogram (ECG) analysis in car drivers. 2019 27th European Signal Processing Conference (EUSIPCO), 1–5. https://doi.org /10.23919/EUSIPCO.2019.8902631