An Improved Myocardial Infarction Detection using Convolutional Neural Network and Graph Neural Network Algorithm

Opeyemi Aderiike Abisoye

doi:10.18495/comengapp.v13i2.438

[1] Allmadhor, A., Rauf, H. T., Lali, M. I. U., Damasevicius, R., Alouffi, B., & Alharbi, A. (2021). AI-driven framework for recognition of guava plant diseases through machine learning from DSLR camera sensor based high resolution imagery. Sensors, 21(11), 3830.
[2] Anand, A., Kadian, T., Shetty, M. K., & Gupta, A. (2022). Explainable AI decision model for ECG data of cardiac disorders. Biomedical Signal Processing and Control, 75, 103584.
[3] Cho, Y., Kwon, J.-m., Kim, K.-H., Medina-Inojosa, J. R., Jeon, K.-H., Cho, S., Lee, S. Y., Park, J., & Oh, B.-H. (2020). Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography. Scientific reports, 10(1), 1-10.
[4] Degerli, A., Zabihi, M., Kiranyaz, S., Hamid, T., Mazhar, R., Hamila, R., & Gabbouj, M. (2021). Early detection of myocardial infarction in low-quality echocardiography. IEEE Access, 9, 34442-34453.
[5] Fatimah, B., Singh, P., Singhal, A., Pramanick, D., Pranav, S., & Pachori, R. B. (2021). Efficient detection of myocardial infarction from single lead ECG signal. Biomedical Signal Processing and Control, 68, 102678.
[6] Gupta, D., Bajpai, B., Dhiman, G., Soni, M., Gomathi, S., & Mane, D. (2021). Review of ECG arrhythmia classification using deep neural network. Materials Today: Proceedings.
[7] Hammad, M., Chelloug, S. A., Alkanhel, R., Prakash, A. J., Muthanna, A., Elgendy, I. A., & Plawiak, P. (2022). Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model. Sensors, 22(17), 6503.
[8] Hammad, M., Iliyasu, A. M., Subasi, A., Ho, E. S., & Abd El-Latif, A. A. (2020). A multitier deep learning model for arrhythmia detection. IEEE Transactions on Instrumentation and Measurement, 70, 1-9.
[9] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition,
[10] He, Z., Yuan, Z., An, P., Zhao, J., & Du, B. (2021). MFB-LANN: A lightweight and updatable myocardial infarction diagnosis system based on convolutional neural networks and active learning. Computer Methods and Programs in Biomedicine, 210, 106379.
[11] Ibrahim, L., Mesinovic, M., Yang, K.-W., & Eid, M. A. (2020). Explainable prediction of acute myocardial infarction using machine learning and shapley values. IEEE Access, 8, 210410-210417.
[12] Jahmunah, V., Ng, E., San, T. R., & Acharya, U. R. (2021). Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals. Computers in biology and medicine, 134, 104457.
[13] Khan, M. A., Alhaisoni, M., Tariq, U., Hussain, N., Majid, A., Damasevicius, R., & Maskeliunas, R. (2021). COVID-19 case recognition from chest CT images by deep learning, entropy-controlled firefly optimization, and parallel feature fusion. Sensors, 21(21), 7286.
[14] Menyar, A. A. (2006). Drug-induced myocardial infarction secondary to coronary artery spasm in teenagers and young adults. Journal of postgraduate medicine, 52(1), 51.
[15] Palczynski, K., Smigiel, S., Ledzinski, D., & Bujnowski, S. (2022). Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset. Sensors, 22(3), 904.
[16] Prabhakararao, E., & Dandapat, S. (2021). Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification. IEEE Journal of Biomedical and Health Informatics.
[17] Prakash, A. J., Samantray, S., Bala, C. L., & Narayana, Y. (2021). An Automated Diagnosis System for Cardiac Arrhythmia Classification. In Analysis of Medical Modalities for Improved Diagnosis in Modern Healthcare (pp. 301-313). CRC Press.
[18] Pustjens, T., Appelman, Y., Damman, P., Ten Berg, J., Jukema, J., de Winter, R., Agema, W., van der Wielen, M., Arslan, F., & Rasoul, S. (2020). Guidelines for the management of myocardial infarction/injury with non-obstructive coronary arteries (MINOCA): a position paper from the Dutch ACS working group. Netherlands Heart Journal, 28(3), 116-130.
[19] Ramaraj, E. (2021). A novel deep learning based gated recurrent unit with extreme learning machine for electrocardiogram (ECG) signal recognition. Biomedical Signal Processing and Control, 68, 102779.
[20] Sharma, L., & Sunkaria, R. (2020). Myocardial infarction detection and localization using optimal features based lead specific approach. Irbm, 41(1), 58-70.
[21] Sharma, L. D., & Sunkaria, R. K. (2018). Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal, Image and Video Processing, 12(2), 199-206.
[22] Smigiel, S., Palczynski, K., & Ledzinski, D. (2021a). Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset. Sensors, 21(24), 8174.
[23] Smigiel, S., Palczynski, K., & Ledzinski, D. (2021b). ECG signal classification using deep learning techniques based on the PTB-XL dataset. Entropy, 23(9), 1121.
[24] Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021). Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors, 21(8), 2852.
[25] Wagner, P., Strodthoff, N., Bousseljot, R.-D., Kreiseler, D., Lunze, F. I., Samek, W., & Schaeffter, T. (2020). PTB-XL, a large publicly available electrocardiography dataset. Scientific data, 7(1), 1-15. [Record #183 is using a reference type undefined in this output style.]
Zhang, J., Liang, D., Liu, A., Gao, M., Chen, X., Zhang, X., & Chen, X. (2021). MLBF-Net: a multi-lead-branch fusion network for multi-class arrhythmia classification using 12-Lead ECG. IEEE Journal of Translational Engineering in Health and Medicine, 9, 1-11

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Affiliations

Opeyemi Aderiike Abisoye
Federal University of Technology, Minna, Computer Science Dept, Minna, Niger Statee

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An Improved Myocardial Infarction Detection using Convolutional Neural Network and Graph Neural Network Algorithm

Opeyemi Aderiike Abisoye

Vol 13 No 2 (2024)

DOI 10.18495/comengapp.v13i2.438

Submitted: Mar 16, 2023

Published: Jun 1, 2024

Abstract

Myocardial infarction (MI) is a crucial health problem and its mortality rate is higher than that of cancer. It is the damage and death of heart muscle from the sudden blockage of a coronary artery by a blood clot. Although lots of researches have been carried out with impressive performance record for detection of MI, however, existing approaches for MI detection can be improved upon for better performance. A vital piece of medical technology that aids in the diagnosis of a number of heart-related disorders in patients is an electrocardiogram (ECG). To find significant episodes in long-term ECG data, an automated diagnostic method is needed. Cardiologists face a very difficult problem when trying to quickly examine long-term ECG records. To pinpoint critical occurrences, a computer-based diagnosing tool is necessary. In this study we employ Convolutional Neural Network (CNN) algorithm with Graph Neural Network (GNN) to select best features and make appropriate classifications. The result of the study gave f1 score of 99.58%, precision of 99.5% and an accuracy of 99.72%. Our proposed model have shown a significant improvement in the detection of MI, this will aid in effectively addressing the challenge of performance drawback in this domain of research.