2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 8 , Issue 2 , PP: 16-24, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Z. Abualkishik 1 * , Rasha Almajed 2 , Saleh A. Almutairi 3
Doi: https://doi.org/10.54216/FPA.080202
Deaths from cardiovascular disease (CVD) are more common than any other kind of mortality in the world. Electrocardiograms, two-dimensional echocardiograms, and stress tests are only a few of the diagnostic tools available to combat the rising incidence of cardiovascular disease. Since the electrocardiogram (ECG) is a clinical therapeutic agent that does not need any intrusive procedures, it may be used to diagnose cardiovascular disease (CVD) early and prescribe the appropriate treatment to prevent its fatal consequences. However, it may be time-consuming and demanding for a physical examination to interpret all these signals from various pieces of equipment, especially if they are non-stationary and repeating. It is necessary to use a computer-assisted model for rapid and precise prediction of CVDs since the Heart Signal from an ECG machine is not a stationary sign, the differences may not be repeated and may manifest at different intervals. In this paper, we offer a fully deep convolutional neural network-based automated diagnosis technique for cardiovascular illness. In order to extract those form characteristics from the Kaggle cardio-vascular disease dataset, CVD-MRI is employed in this detection method. In this case, the risk of cardiovascular disease is estimated using a completely deep convolution neural network and deep learning convolution filters (CVD). The suggested operation's main goal is to "improve the accuracy of completely deep convolution neural network while simultaneously reducing the complexity of the computation and the cost function." Accuracy of 88 percent is achieved by the proposed fully deep convolutional neural network.
Cardiovascular Disease , Deep Learning Fusion , MRI Image , Model Fusion
[1] J. Metan, A. Y. Prasad, K. S. A. Kumar, M. Mathapati, and K. K. Patil, “Cardiovascular MRI image analysis by using the bio inspired (sand piper optimized) fully deep convolutional network (Bio-FDCN) architecture for an automated detection of cardiac disorders,” Biomedical Signal Processing and Control, vol. 70, p. 103002, 2021.
[2] W. A. AlJaroudi and F. G. Hage, “Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2020: positron emission tomography, computed tomography, and magnetic resonance,” Journal of Nuclear Cardiology, pp. 1–12, 2021.
[3] C. El-Hajj and P. A. Kyriacou, “A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure,” Biomedical Signal Processing and Control, vol. 58, p. 101870, 2020.
[4] W. AlJaroudi and F. G. Hage, “Review of cardiovascular imaging in the Journal of Nuclear Cardiology in 2016. Part 1 of 2: Positron emission tomography, computed tomography and magnetic resonance,” Journal of Nuclear Cardiology, vol. 24, no. 2, pp. 649–656, 2017.
[5] G. Verma et al., “Advances in Diagnostic Techniques for Therapeutic Intervention,” in Biomedical Engineering and its Applications in Healthcare, Springer, 2019, pp. 105–121.
[6] A. K. Lam, “Update on adrenal tumours in 2017 World Health Organization (WHO) of endocrine tumours,” Endocrine pathology, vol. 28, no. 3, pp. 213–227, 2017.
[7] S. Kazemifar et al., “MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach,” Radiotherapy and Oncology, vol. 136, pp. 56–63, 2019.
[8] S. Fahle, C. Prinz, and B. Kuhlenkötter, “Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application,” Procedia CIRP, vol. 93, pp. 413–418, 2020.
[9] F. J. García-Peñalvo and A. J. Mendes, “Exploring the computational thinking effects in pre-university education,” Computers in Human Behavior, vol. 80. Elsevier, pp. 407–411, 2018.
[10] M. F. Alkadri, M. Turrin, and S. Sariyildiz, “A computational workflow to analyse material properties and solar radiation of existing contexts from attribute information of point cloud data,” Building and Environment, vol. 155, pp. 268–282, 2019.
[11] S. Mythili, K. Thiyagarajah, P. Rajesh, and F. H. Shajin, “Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: an antlion optimiser and invasive weed optimisation algorithm,” HKIE Trans, vol. 27, no. 1, pp. 25–37, 2020.
[12] P. Rajesh and F. Shajin, “A multi-objective hybrid algorithm for planning electrical distribution system,” Eur J Electr Eng, vol. 22, no. 4–5, pp. 224–509, 2020.
[13] F. H. Shajin and P. Rajesh, “Trusted secure geographic routing protocol: outsider attack detection in mobile ad hoc networks by adopting trusted secure geographic routing protocol,” International Journal of Pervasive Computing and Communications, 2020.
[14] M. K. Thota, F. H. Shajin, and P. Rajesh, “Survey on software defect prediction techniques,” International Journal of Applied Science and Engineering, vol. 17, no. 4, pp. 331–344, 2020.
[15] R. Li et al., “DeepUNet: A deep fully convolutional network for pixel-level sea-land segmentation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 11, pp. 3954–3962, 2018.
[16] A. Kaur, S. Jain, and S. Goel, “Sandpiper optimization algorithm: a novel approach for solving real-life engineering problems,” Applied Intelligence, vol. 50, no. 2, pp. 582–619, 2020.
[17] W. Bai et al., “Automated cardiovascular magnetic resonance image analysis with fully convolutional networks,” Journal of Cardiovascular Magnetic Resonance, vol. 20, no. 1, pp. 1–12, 2018.
[18] D. Des Mc Lernon and D. L. Mhamdi, “Analysis and Processing physiological data from a watch-like device to detect stress pattern,” The University of Leeds, August2015, 2015.
[19] K. Srivastava and D. K. Choubey, “Heart disease prediction using machine learning and data mining,” International Journal of Recent Technology and Engineering, vol. 9, no. 1, pp. 212–219, 2020.
[20] P. M. Mohan, V. Nagarajan, and S. R. Das, “Stress measurement from wearable photoplethysmographic sensor using heart rate variability data,” in 2016 International Conference on Communication and Signal Processing (ICCSP), 2016, pp. 1141–1144.
[21] J. Zhang, W. Wen, F. Huang, and G. Liu, “Recognition of real-scene stress in examination with heart rate features,” in 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2017, vol. 1, pp. 26–29.
[22] G. Shanmugasundaram, S. Yazhini, E. Hemapratha, and S. Nithya, “A comprehensive review on stress detection techniques,” in 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), 2019, pp. 1–6.
[23] E. Cariou et al., “Diagnostic score for the detection of cardiac amyloidosis in patients with left ventricular hypertrophy and impact on prognosis,” Amyloid, vol. 24, no. 2, pp. 101–109, 2017.
[24] T. Tuncer, S. Dogan, R.-S. Tan, and U. R. Acharya, “Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals,” Information Sciences, vol. 565, pp. 91–104, 2021.
