Trägårdh E, Engblom H, Pahlm O. How many ecg leads do we need? Cardiol Clin. 2006;24(3):317–30.

Article  Google Scholar 

Macfarlane P, Devine B, Clark E. The university of glasgow (uni-g) ecg analysis program. In: Computers in Cardiology, pp 451– 454; 2005. IEEE

Ojha N, Dhamoon AS. Myocardial Infarction. In: StatPearls. StatPearls Publishing, Treasure Island (FL); 2024. http://www.ncbi.nlm.nih.gov/books/NBK537076/ Accessed 2024-03-04

Kwok CS, Bennett S, Azam Z, Welsh V, Potluri R, Loke YK, Mallen CD. Misdiagnosis of acute myocardial infarction: a systematic review of the literature. Crit Pathw Cardiol. 2021;20(3):155–62.

Google Scholar 

Khan AH, Hussain M, Malik MK. Ecg images dataset of cardiac and covid-19 patients. Data Brief. 2021;34:106762.

Article  Google Scholar 

Abubaker MB, Babayiğit B. Detection of cardiovascular diseases in ecg images using machine learning and deep learning methods. IEEE Trans Artif Intell. 2022;4(2):373–82.

Article  Google Scholar 

Anand A, Kadian T, Shetty MK, Gupta A. Explainable ai decision model for ecg data of cardiac disorders. Biomed Signal Process Control. 2022;75:103584.

Article  Google Scholar 

Goldberger AL, Goldberger ZD, Shvilkin A. ECG Leads. In: Goldberger’s Clinical Electrocardiography, pp. 21– 31. Elsevier, Amsterdam, 2018. https://doi.org/10.1016/B978-0-323-40169-2.00004-4. https://linkinghub.elsevier.com/retrieve/ pii/B9780323401692000044 Accessed 2024-03-01

Ashley EA, Niebauer J. Conquering the ecg. cardiology explained, 15–33; 2004

Li Y, Qu Q, Wang M, Yu L, Wang J, Shen L, He K. Deep learning for digitizing highly noisy paper-based ecg records. Comput Biol Med. 2020;127:104077.

Article  Google Scholar 

Mishra S, Khatwani G, Patil R, Sapariya D, Shah V, Parmar D, Dinesh S, Daphal P, Mehendale N. Ecg paper record digitization and diagnosis using deep learning. J Med Biol Eng. 2021;41(4):422–32.

Article  Google Scholar 

Fortune JD, Coppa NE, Haq KT, Patel H, Tereshchenko LG. Digitizing ecg image: a new method and open-source software code. Comput Methods Programs Biomed. 2022;221:106890.

Article  Google Scholar 

Wu H, Patel KHK, Li X, Zhang B, Galazis C, Bajaj N, Sau A, Shi X, Sun L, Tao Y, et al. A fully-automated paper ecg digitisation algorithm using deep learning. Sci Rep. 2022;12(1):20963.

Article  Google Scholar 

Fathail I, Bhagile VD. Ecg paper digitization and r peaks detection using fft. Appl Comput Intell Soft Comput. 2022;2022(1):1238864.

Google Scholar 

Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65–9.

Article  Google Scholar 

Ribeiro AH, Ribeiro MH, Paixão GM, Oliveira DM, Gomes PR, Canazart JA, Ferreira MP, Andersson CR, Macfarlane PW, Meira W Jr, et al. Automatic diagnosis of the 12-lead ecg using a deep neural network. Nat Commun. 2020;11(1):1760.

Article  Google Scholar 

Ganeshkumar M, Ravi V, Sowmya V, Gopalakrishnan E, Soman K. Explainable deep learning-based approach for multilabel classification of electrocardiogram. IEEE Trans Eng Manag. 2021;70(8):2787–99.

Google Scholar 

Sangha V, Mortazavi BJ, Haimovich AD, Ribeiro AH, Brandt CA, Jacoby DL, Schulz WL, Krumholz HM, Ribeiro ALP, Khera R. Automated multilabel diagnosis on electrocardiographic images and signals. Nat Commun. 2022;13(1):1583.

Article  Google Scholar 

Le KH, Pham HH, Nguyen TB, Nguyen TA, Thanh TN, Do CD. Lightx3ecg: a lightweight and explainable deep learning system for 3-lead electrocardiogram classification. Biomed Signal Process Control. 2023;85:104963.

Article  Google Scholar 

Han C, Sun J, Bian Y, Que W, Shi L. Automated detection and localization of myocardial infarction with interpretability analysis based on deep learning. IEEE Trans Instrum Meas. 2023;72:1–12.

Google Scholar 

Qiang Y, Dong X, Yang Y. Automatic detection and localisation of myocardial infarction using multi-channel dense attention neural network. Biomed Signal Process Control. 2024;89:105766.

Article  Google Scholar 

Alamatsaz N, Tabatabaei L, Yazdchi M, Payan H, Alamatsaz N, Nasimi F. A lightweight hybrid cnn-lstm explainable model for ecg-based arrhythmia detection. Biomed Signal Process Control. 2024;90:105884.

Article  Google Scholar 

Abagaro AM, Barki H, Ayana G, Dawud AA, Thamineni BL, Jemal T, Choe S-W. Automated ecg signals analysis for cardiac abnormality detection and classification. J Electr Eng Technol. 2024;19(5):3353–71.

Article  Google Scholar 

Saha A, Bristy EJ, Islam MR, Afzal R, Ridita SA et al. Lime-based explainable ai models for predicting disease from patient’s symptoms. In: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–6; 2023. IEEE

Sanim MS, Hasan KM, et al. Identification of covid-19 from other upper respiratory tract infections using random undersampling and lime-based xai model. In: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1– 6; 2023. IEEE

Akhtar M, Ahmed KA, et al. An improved prediction of polycystic ovary syndrome using smote-based oversampling and stacking classifier. In: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1– 6; 2023. IEEE

Robbani R, Alam MM, Sanim MS, Hasan KM. Adaseml: Hospitalization period prediction of covid-19 patients using adasyn and stacking based ensemble learning. In: International Conference on Advanced Network Technologies and Intelligent Computing, pp 3–15; 2022. Springer

Xiong P, Lee SM-Y, Chan G. Deep learning for detecting and locating myocardial infarction by electrocardiogram: A literature review. Front Cardiovasc Med. 2022;9:860032.

Article  Google Scholar 

Denysyuk HV, Pinto RJ, Silva PM, Duarte RP, Marinho FA, Pimenta L, Gouveia AJ, Gonçalves NJ, Coelho PJ, Zdravevski E, et al. Algorithms for automated diagnosis of cardiovascular diseases based on ecg data: a comprehensive systematic review. Heliyon. 2023;9(2):e13601.

Article  Google Scholar 

Rodner E, Simon M, Fisher RB, Denzler J. Fine-grained recognition in the noisy wild: Sensitivity analysis of convolutional neural networks approaches; 2016. arXiv preprint arXiv:1610.06756

Grinsztajn L, Oyallon E, Varoquaux G. Why do tree-based models still outperform deep learning on typical tabular data? Adv Neural Inf Process Syst. 2022;35:507–20.

Google Scholar 

Ashokan PL, Sathya SS. Comparing tree-based ensemble machine learning models and convolutional neural network for automatic classification of heart abnormalities from ecg images. In: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1– 6; 2023. IEEE

Anwar T, Zakir S. Effect of image augmentation on ecg image classification using deep learning. In: 2021 International Conference on Artificial Intelligence (ICAI), pp. 182–186; 2021. IEEE

Meek S, Morris F. Introduction. i–leads, rate, rhythm, and cardiac axis. BMJ. 2002;324(7334):415–8.

Article  Google Scholar 

Khan AH, Hussain M, Malik MK. Cardiac disorder classification by electrocardiogram sensing using deep neural network. Complexity. 2021;2021:1–8.

Google Scholar 

Goldberger AL, Goldberger ZD, Shvilkin A. Chapter 4—ecg leads. In: Goldberger AL, Goldberger ZD, Shvilkin A (eds) Goldberger’s clinical electrocardiography, 9th Edn, pp. 21– 31. Elsevier, Amsterdam (2018). https://doi.org/10.1016/B978-0-323-40169-2.00004-4 . https://www.sciencedirect.com/science/article/pii/B9780323401692000044

Leong LH, Yap YC, Lo ZZ. Importance of lead avl in the diagnosis of inferior wall myocardial infarction: A case report. Malaysn Fam Phys. 2023;18:38.

Article  Google Scholar 

George A, Arumugham PS, Figueredo VM. avr-the forgotten lead. Exp Clin Cardiol. 2010;15(2):36.

Google Scholar