Analysis of Machine Learning and Deep Learning Methodologies for Classification and Detection of Arrhythmia
Keywords:
ECG, Discrete wavelet transform (DWT), Arrythmia, Support Vector Machine (SVM), Principle Component AnalysisAbstract
Arrhythmias can be extremely important in the diagnosis and management of cardiac disorders. In this research, we offer a feature extraction and support vector machine (SVM) based technique for ECG arrhythmia detection and classification. The suggested process entails extracting a variety of characteristics, including the R peak, QRS complex, and ST segment, and then utilizing the mutual information criteria to choose the aspects that are most pertinent. The identification and categorization of arrhythmias on the electrocardiogram (ECG) are essential steps in the diagnosis and management of cardiovascular disorders. Recent years have seen the application of feature extraction, machine learning (ML), and deep learning (DL) approaches to the identification and categorization of ECG arrhythmias. In this study, we cover the comparative evaluation of feature extraction, ML, and DL approaches for ECG arrhythmia detection and classification. We first discuss the history of ECG arrhythmia detection and classification before going into relevant research in the field. The approach for ECG data processing, feature extraction, and classification using ML and DL algorithms is then described. In MATLAB, we mimic the suggested methods and report the findings of our tests. Finally, we compare the effectiveness of various techniques and analyze their advantages, disadvantages, applications, and architecture.
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Wang, D.; Meng, Q.; Chen, D.; Zhang, H.; Xu, L. Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal. Sensors 2020, 20, 1579.
Lin, T.-Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; Belongie, S. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 22–25 July 2017; pp. 2117–2125.
Acharya, U.R.; Fujita, H.; Lih, O.S.; Hagiwara, Y.; Tan, J.H.; Adam, M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci. 2017, 405, 81–90. [CrossRef]
Hannun, A.Y.; Rajpurkar, P.; Haghpanahi, M.; Tison, G.H.; Bourn, C.; Turakhia, M.P.; Ng, A.Y. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 2019, 25, 65. [CrossRef]
Yıldırım, Ö.; Pławiak, P.; Tan, R.-S.; Acharya, U.R. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput. Biol. Med. 2018, 102, 411–420. [CrossRef] [PubMed]
Golrizkhatami, Z.; Acan, A. ECG classification using three-level fusion of different feature descriptors. Expert Syst. Appl. 2018, 114, 54–64. [CrossRef]
Guanglong, M.; Xiangqing, W.; Junsheng, Y. ECG Signal Classification Algorithm Based on Fusion Features; Journal of Physics: Conference Series; IOP Publishing: Bristol, UK, 2019; p. 012003.
Cakaloglu, T.; Xu, X. A multi-resolution word embedding for document retrieval from large unstructured knowledge bases. arXiv 2019, arXiv:1902.00663.
Hu, J.; Shen, L.; Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake, UT, USA, 18–22 June 2018; pp. 7132–7141.
Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; Liu, T.-Y. Lightgbm: A highly efficient gradient boosting decision tree. In Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, 4–9 December 2017; pp. 3146–3154.
Shorten, C.; Khoshgoftaar, T.M. A survey on image data augmentation for deep learning. J. Big Data 2019, 6, 60. [CrossRef]
DeVries, T.; Taylor, G.W. Improved regularization of convolutional neural networks with cutout. arXiv 2017, arXiv:1708.04552.
Ali Khazaee and Zadeh A. E. “ECG beat classification using particle swarm optimization and support vector machine”, Frontiers of Computer Science, 2014, Vol. 8: Issue 2, pp217-231.
Almenar V. andAlbiol A. “A new adaptive scheme for ECG enhancement”, Signal Process, 1999, Vol. 75: Issue 3, pp253-263.
Anapagamini S. A. andRajavel R.“Hardware implementation of ECG denoising system using TMS320C6713 DSP Processor”, International Journal of Biomedical Engineering andTechnology, 2016, Vol. 21: Issue 1, pp95-108.
Andreao, Rodrigo Varejao, Bernadette Dorizzi, and Jerome Boudy. “ECG signal analysis through hidden Markov models”, IEEE Transactions on Biomedical Engineering, 2006, Vol. 53: Issue 8, pp1541-1549.
Anil Chacko “A Study on Enhancement Techniques for Electrocardiogram Signals” Thesis Dissertation ,2012.
Argyro Kampouraki, George Manis, and Christophoros Nikou. “Heartbeat time series classification with support vector machines”, IEEE Transactions on Information Technology in Biomedicine, 2009, Vol. 13: Issue 4, pp512-518.
Arnold M. Katz “Physiology of the Heart”, 5th edition, 2011, Lippincott Williams and Wilkins, Philadelphia, ISBN 10:1-60831-171-6.
Ashfanoor Kabir Md. and Celia Shahnaz “Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains”, Biomedical Signal Processing and Control, 2012, Vol.7: Issue 5, pp481–489.
Association for the Advancement of Medical Instrumentation-American National Standards Institute, Ambulatory electrocardiographs, publication notes: ANSI/AAMI EC38-1994.
Atiyeh Karimipourand Mohammad Reza Homaeinezhad “Real-time electrocardiogram P-QRS-T detection–delineation algorithm based on qualitysupported analysis of characteristic templates”, Computers in Biology and Medicine, 2014, Vol. 52:Issue 1, pp153–165.
Banerjee Swati andMadhuchhandaMitra “ECG Signal Denoisingand QRS Complex Detection by Wavelet Transform Based Thresholding”, Sensors and Transducers Journal, 2010, Vol. 119: Issue 8, pp207-214.
Barrett, Paddy M., Ravi Komatireddy, Sharon Haaser, Sarah Topol, Judith Sheard, Jackie Encinas, Angela J. Fought, and Eric J. Topol.“Comparison of 24-hour Holter monitoring with 14-day novel Adhesive Patch Electrocardiographic Monitoring”, The American Journal of Medicine, 2014, Vol. 127: Issue 1, pp95.e11-95.e17.
Beatriz A. Garro1 and Roberto A. Vázquez2 “Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms”,Hindawi Publishing Corporation, Computational Intelligence and Neuroscience, 2015, Vol. 2015, pp1-20.
Bendiabdallah, Mohammed Hakim, Mohamed Amine Chikh, and BelabbasYagoubi. “Modified fuzzy ARTMAP for cardiac arrhythmia recognition”, International Journal of Biomedical Engineering and Technology, Vol. 22: Issue 1, pp79-97.
Bhogeshwar, SandeSeema, M. K. Soni, and Dipali Bansal. “To verify and compare denoising of ECG signal using various denoising algorithms of IIR and FIR filters. International Journal of Biomedical Engineering and Technology”, 2014, Vol.16: Issue 3, pp244-267.
Blanco-Velasco, Manuel, BinweiWeng, and Kenneth E. Barner. “ECG signal denoisingandbaseline wander correction based on the empirical mode decomposition", Computers in Biology and Medicine, 2008, Vol. 38: Issue 1, pp1- 13.
Carlisle A. And Dozier G. “An Off-The-Shelf PSO”, Proceedings of the Particle Swarm Optimization Workshop, 2001, Vol. 1, pp1-6.
S. K. A., A.. Raj, V. Sharma, and V. Kumar.“Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language Using CNN”." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (2022): 10-14.
Mittall, Nidhi; Saini, Yashika; Mukhija, Manish Kumar; Alaria, Satish Kumar, Design Implementation and Assessment of Efficient Brain Tumor Detection and Classification System Using Improved Machine Learning Techniques, Turkish Online Journal of Qualitative Inquiry . 2021, Vol. 12 Issue 8, p4124-4140. 17p.
Banerjee, S. ., Chakraborty, S. ., & Mondal, A. C. . (2023). Machine Learning Based Crop Prediction on Region Wise Weather Data. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 145–153. https://doi.org/10.17762/ijritcc.v11i1.6084
Khatri, K. ., & Sharma, D. A. . (2020). ECG Signal Analysis for Heart Disease Detection Based on Sensor Data Analysis with Signal Processing by Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 1(1), 06–10. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/11
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