Some people can find it difficult to comprehend the ECG report (graph). It would be less complicated if there was a program that could interpret the ECG data and provide the patient advice on the best course of action to take right away. The patient's state is often classified as either "normal" or "abnormal" on an ECG report. However, it is not much simpler to grasp the graph after utilizing this little analysis. An ECG Graph Analyzer should come to the user's aid if they find themselves in a position where they are unable to visit a qualified doctor to analyze their findings. In this survey study, we examine numerous cutting-edge techniques applied to solve this problem.
Keywords
ECG, ANN, Deep Learning, Machine Learning, ECG Signals
Aziz, S., Ahmed, S. and Alouini, MS. ECG-based machine-learning algorithms for heartbeat classification. Sci Rep 11, 18738 (2021). https://doi.org/10.1038/s41598-021-97118-5.
Jakub Parák, Jan Havlík, ECG Signal Processing and Heart Rate Frequency Detection Methods, “https://www.researchgate.net/publication/266281892”, Conference Paper: November 2011.
Mehmet Ustundag, Muammer Gokbulut, Abdulkadir Sengur, Fikret Ata, Denoising of weak ECG signals by using wavelet analysis and fuzzy thresholding, “DOI 10.1007/s13721-012-0015-5”, Netw Model Anal Health Inform Bioinforma.
Priyanka Mayapur, Classification of Arrhythmia from ECG Signals using MATLAB,” DOI:10.31033/ijemr.8.6.11”, International Journal of Engineering and Management Research.
Siddharth Mishra, Gaurav Khatwani, Rupali Patil, Darshan Sapariya, Vruddhi Shah, Darsh Parmar, Sharath Dinesh, Prathamesh Daphal, Ninad Mehendale, ECG Paper Record Digitization and Diagnosis Using Deep Learning, “https://doi.org/10.1007/s40846-021-00632-0" Journal of Medical and Biological Engineering (2021) 41:422–432.
Attia, Z.I., Kapa, S., Lopez-Jimenez, F. et al. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med 25, 70–74 (2019). https://doi.org/10.1038/s41591-018-0240-2.
Alarsan, F.I., Younes, M. Analysis and classification of heart diseases using heartbeat features and machine learning algorithms. J Big Data 6, 81 (2019). https://doi.org/10.1186/s40537-019-0244-x.
Saman Parvaneh, Jonathan Rubin, Saeed Babaeizadeh, Minnan Xu-Wilson, Cardiac arrhythmia detection using deep learning: A review, Journal of Electrocardiology, Volume 57, Supplement, 2019, Pages S70-S74, ISSN 0022-0736, https://doi.org/10.1016/j.jelectrocard.2019.08.004.
Sampson, Michael and Mcgrath, Anthony. (2015). Understanding the ECG. Part 1: Anatomy and physiology. British Journal of Cardiac Nursing. 10. 548-554. 10.12968/bjca.2015.10.11.548.
N. Strodthoff, P. Wagner, T. Schaeffter and W. Samek, "Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 5, pp. 1519-1528, May 2021, doi: 10.1109/JBHI.2020.3022989.
Cite this article
Neha Sara Abraham, Dipika Ray, Harshitha A Reddy, S Jessy Joyner, Sahana J, “A Comprehensive Survey on ECG Signal Graph Interpretation”, Advances in Intelligent Systems and Technologies, pp. 082-086, December. 2022. doi: 10.53759/aist/978-9914-9946-1-2_15
