REFERENCES

1. Roth GA, Abate D, Abate KH, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392:1736-88.

2. di Bernardo D, Murray A. Explaining the T-wave shape in the ECG. Nature. 2000;403:40.

3. Attia ZI, Harmon DM, Dugan J, et al. Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction. Nat Med. 2022;28:2497-503.

4. Holst H, Ohlsson M, Peterson C, Edenbrandt L. A confident decision support system for interpreting electrocardiograms. Clin Physiol. 1999;19:410-8.

5. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Trans Biomed Eng. 1985;32:230-6.

6. Belforte G, De Mori R, Ferraris F. A contribution to the automatic processing of electrocardiograms using syntactic methods. IEEE Trans Biomed Eng. 1979;26:125-36.

7. Raghunath S, Ulloa Cerna AE, Jing L, et al. Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nat Med. 2020;26:886-91.

8. Lence A, Extramiana F, Fall A, Salem JE, Zucker JD, Prifti E. Automatic digitization of paper electrocardiograms - a systematic review. J Electrocardiol. 2023;80:125-32.

9. Poterucha TJ, Jing L, Ricart RP, et al. Detecting structural heart disease from electrocardiograms using AI. Nature. 2025;644:221-30.

10. Al-Zaiti SS, Martin-Gill C, Zègre-Hemsey JK, et al. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med. 2023;29:1804-13.

11. Attia ZI, Harmon DM, Behr ER, Friedman PA. Application of artificial intelligence to the electrocardiogram. Eur Heart J. 2021;42:4717-30.

12. Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol. 2021;18:465-78.

13. Kligfield P, Badilini F, Brown B, Helfenbein E, Kohls M. The ISCE ECG genome pilot challenge: a 2004 progress report. J Electrocardiol. 2004;37 Suppl:144-8.

14. Stockbridge N. Points to consider in electrocardiogram waveform extraction. J Electrocardiol. 2005;38:319-20.

15. Badilini F, Erdem T, Zareba W, Moss AJ. ECGScan: a method for conversion of paper electrocardiographic printouts to digital electrocardiographic files. J Electrocardiol. 2005;38:310-8.

16. Wu H, Patel KHK, Li X, et al. A fully-automated paper ECG digitisation algorithm using deep learning. Sci Rep. 2022;12:20963.

17. Randazzo V, Puleo E, Paviglianiti A, Vallan A, Pasero E. Development and validation of an algorithm for the digitization of ECG paper images. Sensors. 2022;22:7138.

18. Wang T, Wang YQ, Chang ZS, Lyu X, Huang ZP, Qin F. A fully-automated paper ECG digitisation algorithm for e-health applications. In: 2024 16th International Conference on Wireless Communications and Signal Processing (WCSP); 2024 Oct 24-26; Hefei, China. IEEE; 2024. pp. 1295-300.

19. Ganesh S, Bhatti PT, Alkhalaf M, Gupta S, Shah AJ, Tridandapani S. Combining optical character recognition with paper ECG digitization. IEEE J Transl Eng Health Med. 2021;9:1900309.

20. Li Y, Qu Q, Wang M, et al. Deep learning for digitizing highly noisy paper-based ECG records. Comput Biol Med. 2020;127:104077.

21. Patil R, Narkhede B, Varma S, Suraliya S, Mehendale N. Auto lead extraction and digitization of ECG paper records using cGAN. ArXiv. 2022;arXiv:2211.06720.

22. Shivashankara KK, Deepanshi, Shervedani AM, Clifford GD, Reyna MA, Sameni R. ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization. Physiol Meas. 2024;45:055019.

23. Reyna MA, Deepanshi, Weigle J, et al. Ecg-image-database: a dataset of ecg images with real-world imaging and scanning artifacts; a foundation for computerized ecg image digitization and analysis. ArXiv. 2024;arXiv:2409.16612.

24. 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.

25. Qin X, Zhang Z, Huang C, Dehghan M, Zaiane OR, Jagersand M. U2-Net: going deeper with nested U-structure for salient object detection. Pattern Recognition. 2020;106:107404.

26. Liu Z, Mao H, Wu CY, Feichtenhofer C, Darrell T, Xie SN. A ConvNet for the 2020s. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2022 Jun 18-24; New Orleans, LA, USA. IEEE; 2022. pp. 11966-76.

27. Woo S, Park J, Lee JY, Kweon IS. CBAM: convolutional block attention module. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, Editors. Computer Vision - ECCV 2018: 15th European Conference; 2018 Sep 8-14; Munich, Germany. Springer, Cham; 2018. pp. 3-19.

28. Lin TY, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV); 2017 Oct 22-29; Venice, Italy. IEEE; 2017. pp. 2999-3007.

29. Milletari F, Navab N, Ahmadi SA. V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV); 2016 Oct 25-28; Stanford, CA, USA. IEEE; 2016. pp. 565-71.

30. International Electrotechnical Commission. IEC 60601-2-25:2011. Medical electrical equipment - Part 2-25: Particular requirements for the basic safety and essential performance of electrocardiographs. 2nd ed. Geneva: International Electrotechnical Commission; 2011. Available from: https://webstore.iec.ch/en/publication/2636 [accessed 18 July 2026].

31. Baydoun M, Safatly L, Abou Hassan OK, Ghaziri H, El Hajj A, Isma’eel H. High precision digitization of paper-based ECG records: a step toward machine learning. IEEE J Transl Eng Health Med. 2019;7:1900808.