Interpretation of ECG recordings using artificial intelligence

Goran Damnjanović; Vitomir Perić

doi:10.71159/icemit2516D

Authors

Goran Damnjanović Niš Military Hospital, Niš, Serbia
Vitomir Perić Clinical Hospital Center, Kosovska Mitrovica, Serbia

DOI:

https://doi.org/10.71159/icemit2516D

Keywords:

artificial intelligence, electrocardiogram, ECG analysis, cardiovascular diseases

Abstract

Artificial intelligence (AI) has emerged as a transformative tool in the analysis and interpretation of electrocardiograms. Using machine learning and deep learning algorithms, AI systems can now detect arrhythmias, ischemic changes, and other cardiac abnormalities with a level of speed and accuracy that rivals or complements human expertise. These advances have made it possible to automate ECG interpretation, reduce diagnostic errors, and provide rapid decision support in clinical and remote settings. In addition, AI models trained on large ECG datasets can continuously learn and adapt, enabling precision medicine and proactive patient care. With continuous research and thoughtful integration into clinical workflows, AI can become a transformative tool in the early identification and management of heart conditions, ultimately improving care delivery and outcomes in various healthcare settings.

References

Acharya, U. R., Fujita, H., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, 415–416, 190–198. https://doi.org/10.1016/j.ins.2017.06.027

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al‑Dujaili, A., Duan, Y., Al‑Shamma, O., Santamarí, J., Fadhel, M. A., Al‑Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 53. https://doi.org/10.1186/s40537-021-00444-8

Arbelo, E., Protonotarios, A., Gimeno, J. R., Arbustini, E., Barriales-Villa, R., Basso, C., Bezzina, C. R., Biagini, E., Blom, N. A., de Boer, R. A., De Winter, T., Elliott, P. M., Flather, M., Garcia-Pavia, P., Haugaa, K. H., Ingles, J., Jurcut, R. O., Klaassen, S., Limongelli, G., … Kaski, J. P. (2023). 2023 ESC guidelines for the management of cardiomyopathies. European Heart Journal, 44(37), 3503–3626. https://doi.org/10.1093/eurheartj/ehad194

Attia, Z. I., DeSimone, C. V., Dillon, J. J., Sapir, Y., Somers, V. K., Dugan, J. L., Bruce, C. J., Ackerman, M. J., Asirvatham, S. J., Striemer, B. L., Bukartyk, J., Scott, C. G., Bennet, K. E., Ladewig, D. J., Gilles, E. J., Sadot, D., Geva, A. B., & Friedman, P. A. (2016). Novel bloodless potassium determination using a signal-processed single-lead ECG. Journal of the American Heart Association, 5(1), e002746. https://doi.org/10.1161/JAHA.115.002746

Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., Asirvatham, S. J., Scott, C. G., Carter, R. E., & Friedman, P. A. (2019). Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nature Medicine, 25, 70–74. https://doi.org/10.1038/

s41591-018-0240-2

Baxt, W. G., Shofer, F. S., Sites, F. D., & Hollander, J. E. (2002). A neural computational aid to the diagnosis of acute myocardial infarction. Annals of Emergency Medicine, 39, 366–373. https://10.1067/mem.2002.122705

Bouzid, Z., Faramand, Z., Gregg, R. E., Frisch, S. O., Martin-Gill, C., Saba, S., Callaway, C., Sejdić, E., & Al-Zaiti, S. (2021). In search of an optimal subset of ECG features to augment the diagnosis of acute coronary syndrome at the emergency department. Journal of the American Heart Association, 10, e017871. https://doi.org/10.1161/JAHA.120.017871

Byrne, R. A., Rossello, X., Coughlan, J. J., Barbato, E., Berry, C., Chieffo, A., Claeys, M. J., Dan, G.-A., Dweck, M. R., Galbraith, M., Gilard, M., Hinterbuchner, L., Jankowska, E. A., Jüni, P., Kimura, T., Kunadian, V., Leosdottir, M., Lorusso, R., Pedretti, R. F. E., … Ibanez, B. (2023). 2023 ESC guidelines for the management of acute coronary syndromes. European Heart Journal, 44(38), 3720–3826. https://doi.org/10.1093/eurheartj/ehad191

Carrington, M., Providência, R., Chahal, C. A. A., Ricci, F., Epstein, A. E., Gallina, S., Fedorowski, A., Sutton, R., & Khanji, M. Y. (2022). Monitoring and diagnosis of intermittent arrhythmias: Evidence-based guidance and role of novel monitoring strategies. European Heart Journal Open, 2, oeac072. https://doi.org/10.1093/ehjopen/oeac072

Chen, X., Guo, W., Zhao, L., Huang, W., Wang, L., Sun, A., Lang, L., & Mo, F. (2021). Acute myocardial infarction detection using deep learning-enabled electrocardiograms. Frontiers in Cardiovascular Medicine, 8, 654515. https://doi.org/10.3389/fcvm.2021.654515

Cheng, Q., & Dong, Y. (2022). Da Vinci robot-assisted video image processing under artificial intelligence vision processing technology. Computational and Mathematical Methods in Medicine, 2022, 2752444. https://doi.org/10.1155/2022/2752444

Cohen-Shelly, M., Attia, Z. I., Friedman, P. A., Ito, S., Essayagh, B., Ko, W., Murphree, D. H., Michelena, H. I., Enriquez-Sarano, M., Carter, R. E., Johnson, P. W., Noseworthy, P. A., Lopez-Jimenez, F., & Oh, J. K. (2021). Electrocardiogram screening for aortic valve stenosis using artificial intelligence. European Heart Journal, 42, 2885–2896. https://doi.org/10.1093/eurheartj/ehab153

Dorr, M., Nohturfft, V., Brasier, N., Bosshard, E., Djurdjevic, A., Gross, S., Raichle, C. J., Rhinisperger, M., Stöckli, R., & Eckstein, J. (2019). The WATCH AF Trial: SmartWATCHes for detection of atrial fibrillation. JACC: Clinical Electrophysiology, 5, 199–208. https://doi.org/10.1016/

j.jacep.2018.10.006

Elliott, P. M., Gimeno, J. R., Thaman, R., Shah, J., Ward, D., Dickie, S., Esteban, M. T. T., & McKenna, W. J. (2006). Historical trends in reported survival rates in patients with hypertrophic cardiomyopathy. Heart, 92(6), 785–791. https://doi.org/10.1136/hrt.2005.068577

Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., Ackerman, M. J., Noseworthy, P. A., Dillon, J. J., & Friedman, P. A. (2019). Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiology, 4(5), 428–436. https://doi.org/

1001/jamacardio.2019.0640

Gladstone, D. J., Spring, M., Dorian, P., Panzov, V., Thorpe, K. E., Hall, J., Vaid, H., O’Donnell, M., Laupacis, A., Côté, R., Sharma, M., Blakely, J. A., Shuaib, A., Hachinski, V., Coutts, S. B., Sahlas, D. J., Teal, P., Yip, S., Spence, J. D., … Mamdani, M. (2014). Atrial fibrillation in patients with cryptogenic stroke. New England Journal of Medicine, 370, 2467–2477. https://doi.org/10.1056/

NEJMoa1311376

Gumpfer, N., Grun, D., Hannig, J., Keller, T., & Guckert, M. (2020). Detecting myocardial scar using electrocardiogram data and deep neural networks. Biological Chemistry, 402(8), 911–923. https://doi.org/10.1515/hsz-2020-0169

Hagendorff, A., Knebel, F., Helfen, A., Knierim, J., Sinning, C., Stobe, S., Fehske, W., & Ewen, S. (2020). Expert consensus document on the assessment of the severity of aortic valve stenosis by echocardiography to provide diagnostic conclusiveness by standardized verifiable documentation. Clinical Research in Cardiology, 109(3), 271–288. https://doi.org/10.1007/s00392-019-01539-2

Hindricks, G., Potpara, T., Dagres, N., Arbelo, E., Bax, J. J., Blomström-Lundqvist, C., Boriani, G., Castella, M., Dan, G.-A., Dilaveris, P. E., Fauchier, L., Filippatos, G., Kalman, J. M., La Meir, M., Lane, D. A., Lebeau, J.-P., Lettino, M., Lip, G. Y. H., Pinto, F. J., … Watkins, C. L. (2021). 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). European Heart Journal, 42, 373–498. https://doi.org/10.1093/eurheartj/ehaa612

Hopkins, C. B., Suleman, J., & Cook, C. (2000). An artificial neural network for the electrocardiographic diagnosis of left ventricular hypertrophy. Critical Reviews in Biomedical Engineering, 28(3–4), 435–438. https://doi.org/10.1615/CritRevBiomedEng.v28.i34.140

Hygrell, T., Viberg, F., Dahlberg, E., Charlton, P. H., Gudmundsdottir, K. K., Mant, J., Hornlund, J. L., & Svennberg, E. (2023). An artificial intelligence–based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening. EP Europace, 25(7), 1332–1338. https://doi.org/10.1093/europace/euad036

Kagiyama, N., Piccirilli, M., Yanamala, N., Shrestha, S., Farjo, P. D., Casaclang-Verzosa, G., Tarhuni, W. M., Nezarat, N., Budoff, M. J., Narula, J., & Sengupta, P. P. (2020). Machine learning assessment of left ventricular diastolic function based on electrocardiographic features. Journal of the American College of Cardiology, 76, 930–941. https://doi.org/10.1016/j.jacc.2020.06.061

Kang, D. H., Park, S. J., Lee, S. A., Lee, S., Kim, D. H., Kim, H. K., Yun, S. C., Hong, G. R., Song, J. M.; Chung, C. H., Song, J. K., Lee, J. W., & Park, S. W. (2020). Early Surgery or Conservative Care for Asymptomatic Aortic Stenosis. The New England Journal of Medicine, 382, 111–119. https://doi.org/10.1056/NEJMoa1912846

Kashou, A. H., Noseworthy, P. A., Beckman, T. J., Anavekar, N. S., Cullen, M. W., Angstman, K. B., Sandefur, J. B., Shapiro, B. P., Wiley, B. W., Kates, A. M., Huneycutt, D., Braisted, A., Smith, S. W., Baranchuk, A., Grauer, K., O’Brien, K., Kaul, V., Gambhir, H. S., Knohl, S. J., … May, A. M. (2023). ECG interpretation proficiency of healthcare professionals. Current Problems in Cardiology, 48, 101924. https://doi.org/10.1016/j.cpcardiol.2023.101924

Kim, J. H., Cisneros, T., Nguyen, A., van Meijgaard, J., & Warraich, H. J. (2024). Geographic disparities in access to cardiologists in the United States. Journal of the American College of Cardiology, 84, 315–316. https://doi.org/10.1016/j.jacc.2024.04.054

Ko, W.-Y., Siontis, K. C., Attia, Z. I., Carter, R. E., Kapa, S., Ommen, S. R., Demuth, S. J., Ackerman, M. J., Gersh, B. J., Arruda-Olson, A. M., Geske, J. B., Asirvatham, S. J., Lopez-Jimenez, F., Nishimura, R. A., Friedman, P. A., & Noseworthy, P. A. (2020). Detection of hypertrophic cardiomyopathy using a convolutional neural network–enabled electrocardiogram. Journal of the American College of Cardiology, 75(7), 722–733. https://doi.org/10.1016/j.jacc.2019.12.030

Kwon, J., Lee, S. Y., Jeon, K., Lee, Y., Kim, K., Park, J., Oh, B., & Lee, M. (2020). Deep learning-based algorithm for detecting aortic stenosis using electrocardiography. Journal of the American Heart Association, 9, e014717. https://doi.org/10.1161/JAHA.119.014717

Lång, K., Josefsson, V., Larsson, A.-M., Larsson, S., Högberg, C., Sartor, H., Hofvind, S., Andersson, I., & Rosso, A. (2023). Artificial intelligence-supported screen reading versus standard double reading in the mammography screening with artificial intelligence trial (MASAI). The Lancet Oncology, 24, 936–944. https://doi.org/10.1016/S1470-2045(23)00298-X

Lin, C.-S., Lin, C., Fang, W.-H., Hsu, C.-J., Chen, S.-J., Huang, K.-H., Lin, W.-S., Tsai, C.-S., Kuo, C.-C., Chau, T., Yang, S. J., & Lin, S.-H. (2020). A deep-learning algorithm (ECG12Net) for detecting hypokalemia and hyperkalemia by electrocardiography: Algorithm development. JMIR Medical Informatics, 8(3), e15931. https://doi.org/10.2196/15931

Lipshultz, S. E., Orav, E. J., Wilkinson, J. D., Towbin, J. A., Messere, J. E., Lowe, A. M., Sleeper, L. A., Cox, G. F., Hsu, D. T., Canter, C. E., Hunter, J. A., & Colanet, S. D. (2013). Risk stratification at diagnosis for children with hypertrophic cardiomyopathy: An analysis of data from the Pediatric Cardiomyopathy Registry. The Lancet, 382, 1889–1897. https://doi.org/10.1016/S0140-6736(13)61685-2

Masataka, S., Satoshi, K., Naoto, S., Kengo, T., Shunichi, K., Junji, K., Mike, S., Mamoru, N., Hisataka, M., Hideo, F., Nahoko, K., Hiroyuki, W., Minami, S., Masao T., Naoko S., Masao, Y., Shinnosuke, S., Susumu, K., Hiroki, S., Norifumi, T., Katsuhito, F., Masao, D., Hiroshi, A., Hiroyuki, M., Issei, K. (2023). Deep learning models for predicting left heart abnormalities from single-lead electrocardiogram for the development of wearable devices. Circulation Journal, 88(1), 146–156. https://doi.org/10.1253/circj.CJ-23-0216

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27(4), 12. https://doi.org/10.1609/aimag.v27i4.1904

McDonagh, T. A., Metra, M., Adamo, M., Gardner, R. S., Baumbach, A., Böhm, M., Burri, H., Butler, J., Čelutkienė, J., Chioncel, O., Cleland, J. G. F., Coats, A. J. S., Crespo-Leiro, M. G., Farmakis, D., Gilard, M., Heymans, S., Hoes, A. W., Jaarsma, T., Jankowska, E. A., Lainscak, M., … Skibelund, A. K. (2021). 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. European Heart Journal, 42(36), 3599–3726. https://doi.org/10.1093/eurheartj/ehab368

McDonagh, T. A., Metra, M., Adamo, M., Gardner, R. S., Baumbach, A., Böhm, M., Burri, H., Butler, J., Čelutkienė, J., Chioncel, O., Cleland, J. G. F., Crespo-Leiro, M. G., Farmakis, D., Gilard, M., Heymans, S., Hoes, A. W., Jaarsma, T., Jankowska, E. A., Lainscak, M., … Skibelund, A. K. (2023). 2023 focused update of the 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. European Heart Journal, 44(37), 3627–3639. https://doi.org/10.1093/eurheartj/ehad195

McStay, S. (2019). Recording a 12-lead electrocardiogram (ECG). British Journal of Nursing, 28, 756–760. https://doi.org/10.12968/bjon.2019.28.12.756

Omland, T., Aakvaag, A., & Vik-Mo, H. (1996). Plasma cardiac natriuretic peptide determination as a screening test for the detection of patients with mild left ventricular impairment. Heart, 76, 232–237. https://doi.org/10.1136/hrt.76.3.232

Otto, C.M. & Prendergast, B. (2014). Aortic-valve stenosis — From patients at risk to severe valve obstruction. N. Engl. J. Med., 371, 744–756. https://doi.org/10.1056/NEJMra1313875

Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. The New England Journal of Medicine, 381, 1909–1917. https://doi.org/10.1056/NEJMoa1901183

Polimeni, A., Sorrentino, S., De Rosa, S., Spaccarotella, C., Mongiardo, A., Sabatino, J., & Indolfi, C. (2020). Transcatheter versus surgical aortic valve replacement in low-risk patients for the treatment of severe aortic stenosis. Journal of Clinical Medicine, 9, 439. https://doi.org/10.3390/jcm9020439

Quer, G., Arnaout, R., Henne, M., & Arnaout, R. (2021). Machine learning and the future of cardiovascular care: JACC state-of-the-art review. Journal of the American College of Cardiology, 77, 300–313. https://doi.org/10.1016/j.jacc.2020.11.033

Schläpfer, J., & Wellens, H. J. J. (2017). Computer-interpreted electrocardiograms: Benefits and limitations. Journal of the American College of Cardiology, 70, 1183–1192. https://doi.org/

1016/j.jacc.2017.07.723

Schlegel, T. T., Kulecz, W. B., Feiveson, A. H., Greco, E. C., DePalma, J. L., Starc, V., Vrtovec, B., Rahman, M. A., Bungo, M. W., Hayat, M. J., Bauch, T., Delgado, R., Warren, S. G., Núñez-Medina, T., Medina, R., Jugo, D., Arheden, H., & Pahlm, O. (2010). Accuracy of advanced versus strictly conventional 12-lead ECG for detection and screening of coronary artery disease, left ventricular hypertrophy and left ventricular systolic dysfunction. BMC Cardiovascular Disorders, 10, 28. https://doi.org/10.1186/1471-2261-10-28

Shintomi, A., Izumi, S., Yoshimoto, M., & Kawaguchi, H. (2022). Effectiveness of the heartbeat interval error and compensation method on heart rate variability analysis. Healthcare Technology Letters, 9(1–2), 9–15. https://doi.org/10.1049/htl2.12023

Siontis, K. C., Gersh, B. J., Killian, J. M., Noseworthy, P. A., McCabe, P., Weston, S. A., & Chamberlain, A. M. (2016). Typical, atypical, and asymptomatic presentations of new-onset atrial fibrillation in the community: Characteristics and prognostic implications. Heart Rhythm, 13, 1418–1424. https://doi.org/10.1016/j.hrthm.2016.03.003

Soon, S., Svavarsdottir, H., Downey, C., & Jayne, D. G. (2020). Wearable devices for remote vital signs monitoring in the outpatient setting: An overview of the field. BMJ Innovations, 6, 55–60. https://doi.org/10.1136/bmjinnov-2019-000354

Sun, J.-Y., Qiu, Y., Guo, H.-C., Hua, Y., Shao, B., Qiao, Y.-C., Guo, J., Ding, H.-L., Zhang, Z.-Y., Miao, L.-F., Wang, N., Zhang, Y.-M., Chen, Y., Lu, J., Dai, M., Zhang, C.-Y., & Wang, R.-X. (2021). A method to screen left ventricular dysfunction through ECG based on convolutional neural network. Journal of Cardiovascular Electrophysiology, 32, 1095–1102. https://doi.org/10.1111/jce.14936

Upton, R., Mumith, A., Beqiri, A., Parker, A., Hawkes, W., Gao, S., Porumb, M., Sarwar, R., Marques, P., Markham, D., Kenworthy, J., O’Driscoll, J. M., Hassanali, N., Groves, K., Dockerill, C., Woodward, W., Alsharqi, M., McCourt, A., Wilkes, E. H., Heitner, S. B., Yadava, M., Stojanovski, D., Lamata, P., Woodward, G., & Leeson, P. (2022). Automated echocardiographic detection of severe coronary artery disease using artificial intelligence. JACC: Cardiovascular Imaging, 15, 715–727. https://doi.org/10.1016/j.jcmg.2021.10.013

Wexler, R. K., Pleister, A., & Raman, S. (2011). Outpatient approach to palpitations. American Family Physician, 84, 63–69. https://pubmed.ncbi.nlm.nih.gov/21766757/

Yang, Y. C., Islam, S. U., Noor, A., Khan, S., Afsar, W., & Naziret, S. (2021). Influential usage of big data and artificial intelligence in healthcare. Computational and Mathematical Methods in Medicine, 2021, 5812499. https://doi.org/10.1155/2021/5812499

Yao, X., Attia, Z. I., Behnken, E. M., Walvatne, K., Giblon, R. E., Liu, S., Siontis, K. C., Gersh, B. J., Graff-Radford, J., Rabinstein, A. A., Friedman, P. A., & Noseworthy, P. A. (2021). Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: Rationale and design of a digital clinical trial. American Heart Journal, 239, 73–79. https://doi.org/10.1016/j.ahj.2021.05.006

Zhang, Y., Feng, Y., Sun, J., Zhang, L., Ding, Z., Wang, L., Zhao, K., Pan, Z., Li, Q., Guo, N., & Xie, X. (2024). Fully automated artificial intelligence-based coronary CT angiography image processing: Efficiency, diagnostic capability, and risk stratification. European Radiology, 34, 4909–4919. https://doi.org/10.1007/s00330-023-10494-6