Web Based Cardiac Arrhythmia Classification System Using ECG Data Analysis and Machine Learning
Keywords:
Electrocardiogram, Cardiac arrhythmia, Convolutional Neural Network, Support Vector MachineAbstract
The leading reason of death worldwide is due to heart disease and late treatment. The detection and diagnosis of cardiac arrhythmia is tedious and time consuming from arranging expert to analyse a large amount of ECG data. Therefore, detection of cardia arrhythmia by analysis of ECG characteristics using machine learning has become predominant. This paper proposed a web-based system which classify heart disease depending on the patient's ECG values using support vector machine and convolutional neural network. At the end, comparison in between support vector machine and convolutional neural network is also done using evaluation measures like precision, recall and f-measure. This work can be supporting automated tool to cardiologists for the preliminary screening of cardiac arrhythmia patients to know presence or absence of arrhythmia.
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Dangare, C.S., and Apte, S.S., “A Data Mining Approach for Prediction of Heart Disease Using Neural Networks,” International Journal of Computer Engineering and Technology, 3(3), pp. 30-40 ,2012.
Soni, S., Soni, J., Ansari, U., and Sharma, D., “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction,” International Journal of Computer Applications 17, pp. 43-48,2011.
Mendes, D., Paredes, S., Rocha, T., Carvalho, P., Henriques, J., Cabiddu, R., and Morais, J., “Assessment of cardiovascular risk based on a data-driven knowledge discovery approach,” 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6800-6803 ,2015
Collins, F, “Mobile Technology and Healthcare,” Available at http://www.nlm.nih.gov/medlineplus/magazine/issues/winter11.
Aieshwarya, B., ChavanPatil, C., and Sonawane, S.S., “To Predict Heart Disease Risk and Medications Using Data Mining Techniques with an IoT Based Monitoring System for Post-Operative Heart Disease Patients,” Proceeding International Journal on Emerging Trends in Technology, 4(2) ,2017.
Azariadi, D., Tsoutsouras, V., Xydis, S., and Soudris, D., “ECG signal analysis and arrhythmia detection on IoT wearable medical devices. 2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST),” 1-4,2016.
Banu, N., and Swamy, S., “Prediction of heart disease at early stage using data mining and big data analytics: A survey,” 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), pp. 256-261,2016
Aziz, A., and Rehman, A.U., “Detection of Cardiac Disease using Data Mining Classification Techniques,” International Journal of Advanced Computer Science and Applications, 8,2017
Anooj, P.K., “Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules,” Central European Journal of Computer Science, 1, pp. 482-498,2011.
Shouman, M., Turner, T., and Stocker, R., “Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients,” International Journal of Information and Education Technology, pp. 220-223,2012.
Alfaras, M., Soriano, M.C., and Ortin, S., “A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection,” Frontiers in Physics, 7,2019.
Chen, T., Huang, C., Shih, E.S., Hu, Y., and Hwang, M., “Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model,” iScience, 23, 2020.
Sumathi, S. and Agalya, V., “Early Detection of Life-Threatening Cardiac Arrhythmias Using Deep Learning Techniques,” Current Signal Transduction Therapy ,2019.
Thalor, M.A., “Classification and Prediction of Cardiac Arrhythmia using Machine Learning: A Survey,” International Journal for Research in Applied Science and Engineering Technology, 7, pp. 1244-1246,2019.
Sahoo, S., Subudhi, A., Dash, M. et al., “Automatic Classification of Cardiac Arrhythmias Based on Hybrid Features and Decision Tree Algorithm,” International Journal. of Automation and Computing, 17, pp. 551–561,2020.
R. Krishnamoorthy, B. S Liya, S. Arun, S Padmapriya, Gunasundari B, R Thiagarajan, "Categorizing the Heart Syndrome Condition by Predictive Analysis Using Machine Learning Approach", 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), pp.104-108, 2021.
M. Degirmenci, M.A. Ozdemir, E. Izci, A. Akan,Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks,IRBM,2021,
K.S. Park, B.H. Cho, D.H. Lee, S.H. Song, J.S. Lee, Y.J. Chee, I.Y.Kim, S.I. Kim, Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function, in: Comput. Cardiol., 2008, pp.229–232.
Ye, B.V.K. Kumar, M.T. Coimbra, Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification, in: International Conference on Pattern Recognition (ICPR), 2012, pp.2428–2431.
Z. Zhang, J. Dong, X. Luo, K.-S. Choi, X. Wu, Heart beat classification using disease-specific feature selection,Comput. Biol. Med. 46 (2014) 79–89.
Swapna G, Soman KP, Vinayakumar R, Automated detection of cardiac arrhythmia using deep learning techniques , International Conference on Computational Intelligence and Data Science (ICCIDS 2018), Procedia Computer Science 132,1192-1201,1192–1201
Mengze Wu, Yongdi Lu, Wenli Yang and Shen Yuong Wong,A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network, Frontiers in Computational Neuroscience, volume 14, 2021
Roberto F. Automatic heartbeat monitoring system. Arch Case Rep. 2019; 3: 029-034.DOI: 10.29328/journal.acr.1001018
Dr. Govind Shah. (2017). An Efficient Traffic Control System and License Plate Detection Using Image Processing. International Journal of New Practices in Management and Engineering, 6(01), 20 - 25. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/52
Dasari, S. ., Reddy, A. R. M. ., & Reddy , B. E. . (2023). KC Two-Way Clustering Algorithms For Multi-Child Semantic Maps In Image Mining. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 01–11. https://doi.org/10.17762/ijritcc.v11i2s.6023
Kathole, A. B., Katti, J., Dhabliya, D., Deshpande, V., Rajawat, A. S., Goyal, S. B., . . . Suciu, G. (2022). Energy-aware UAV based on blockchain model using IoE application in 6G network-driven cybertwin. Energies, 15(21) doi:10.3390/en15218304
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