Comprehensive Physiology Wiley Online Library

Artificial Intelligence‐Enabled ECG: Physiologic and Pathophysiologic Insights and Implications

Full Article on Wiley Online Library


Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date—the electrocardiogram (ECG). The application of artificial intelligence‐enabled ECG (AI‐ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI‐ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417‐3424, 2022.

Figure 1. Figure 1. AI‐ECG model performance before and after septal myectomy. Artificial intelligence model performance in a 21‐year‐old woman with obstructive hypertrophic cardiomyopathy (HCM) before (A) and after (B) septal myectomy. Prior to myectomy, the patient had massive septal hypertrophy (30 mm). Adapted, with permission, from Ko WY, et al., 2020 29.
Figure 2. Figure 2. Proposed approach to help better understand and discover physiologic patterns detected by AI‐ECG models. (A) Standard multivariate discrimination using known physiologic parameters. (B) Improved diagnostic power with deep learning/CNN. (C) Using these unknown patterns to discover new physiology.

Figure 1. AI‐ECG model performance before and after septal myectomy. Artificial intelligence model performance in a 21‐year‐old woman with obstructive hypertrophic cardiomyopathy (HCM) before (A) and after (B) septal myectomy. Prior to myectomy, the patient had massive septal hypertrophy (30 mm). Adapted, with permission, from Ko WY, et al., 2020 29.

Figure 2. Proposed approach to help better understand and discover physiologic patterns detected by AI‐ECG models. (A) Standard multivariate discrimination using known physiologic parameters. (B) Improved diagnostic power with deep learning/CNN. (C) Using these unknown patterns to discover new physiology.
 1.Arboix A, Marti L, Dorison S, Sanchez MJ. Bayes syndrome and acute cardioembolic ischemic stroke. World J Clin Cases 5: 93‐101, 2017.
 2.Attia ZI, DeSimone CV, Dillon JJ, Sapir Y, Somers VK, Dugan JL, Bruce CJ, Ackerman MJ, Asirvatham SJ, Striemer BL, Bukartyk J, Scott CG, Bennet KE, Ladewig DJ, Gilles EJ, Sadot D, Geva AB, Friedman PA. Novel bloodless potassium determination using a signal‐processed single‐lead ECG. J Am Heart Assoc 5 (1): e002746, 2016.
 3.Attia ZI, Friedman PA, Noseworthy PA, Lopez‐Jimenez F, Ladewig DJ, Satam G, Pellikka PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Kapa S. Age and sex estimation using artificial intelligence from Standard 12‐Lead ECGs. Circ Arrhythm Electrophysiol 12 (9): e007284, 2019.
 4.Attia ZI, Kapa S, Lopez‐Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriguez‐Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence‐enabled electrocardiogram. Nat Med 25: 70‐74, 2019.
 5.Attia ZI, Noseworthy PA, Lopez‐Jimenez F, Asirvatham S, Deshmukh A, Gersh B, Carter R, Yao X, Rabinstein A, Erickson B, Kapa S, Friedman P. An artificial intelligence‐enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction. Lancet 394 (10201): 861‐867, 2019.
 6.Authors/Task Force Members, Elliott PM, Anastasakis A, Borger MA, Borggrefe M, Cecchi F, Charron P, Hagege AA, Lafont A, Limongelli G, Mahrholdt H, McKenna WJ, Mogensen J, Nihoyannopoulos P, Nistri S, Pieper PG, Pieske B, Rapezzi C, Rutten FH, Tillmanns C, Watkins H. ESC guidelines on diagnosis and management of hypertrophic cardiomyopathy: The Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J 35: 2733‐2779, 2014.
 7.Bellotti P, Spirito P, Lupi G, Vecchio C. Left atrial appendage function assessed by transesophageal echocardiography before and on the day after elective cardioversion for nonvalvular atrial fibrillation. Am J Cardiol 81: 1199‐1202, 1998.
 8.Benjamin EJ, Wolf PA, D'Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death the Framingham heart study. Circulation 98 (10): 946‐952, 1998.
 9.Bhattacharya M, Lu D‐Y, Kudchadkar SM, Greenland GV, Lingamaneni P, Corono‐Villalobos CP, Guan Y, Marine JE, Olgin JE, Zimmerman S, Abraham TP, Shatkay H, Abraham MR. Identifying ventricular arrhythmias and their predictors by applying machine learning methods to electronic health records in patients with hypertrophy cardiomyopathy (HCM‐VAr‐Risk Model). Am J Cardiol 15: 1681‐1689, 2019.
 10.Char DS, Shah NH, Magnus D. Implementing machine learning in health care ‐ Addressing ethical challenges. N Engl J Med 378 (11): 981‐983, 2018.
 11.Chen E, Jiang J, Su R, Gao M, Zhu S, Zhou J. A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation. Heart Rhythm 17: 847‐853, 2020.
 12.Ching T, Himmelstein DS, Beaulieu‐Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Boca SM, Swamidass SJ, Huang A, Gitter A, Greene CS. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 15 (141): 20170387, 2018.
 13.Christopoulos G, Graff‐Radford J, Lopez CL, Yao X, Attia ZI, Rabinstein AA, Petersen RC, Knopman DS, Mielke MM, Kremers W, Vemuri P, Siontis KC, Friedman PA, Noseworthy. Artificial intelligence‐electrocardiography to predict incidence atrial fibrillation: A population‐based study. Circ Arrhythm Electrophysiol 13: e009355, 2020.
 14.Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, Gillum RF, Kim YH, McAnulty JH Jr, Zheng ZJ, Forouzanfar MH, Naghavi M, Mensah GA, Ezzati M, Murray CJ. Worldwide epidemiology of atrial fibrillation: A global burden of disease 2010 study. Circulation 129 (8): 837‐847, 2014.
 15.Corrado D, Pelliccia A, Heidbuchel H, Sharma S, Link M, Basso C, Biffi A, Buja G, Delise P, Gussac I, Anastasakis A, Borjesson M, Bjornstad HH, Carre F, Deligiannis A, Dugmore D, Fagard R, Hoogsteen J, Mellwig KP, Panhuyzen‐Goedkoop N, Solberg E, Vanhees L, Drezner J, Estes NA 3rd, Iliceto S, Maron BJ, Peidro R, Schwartz PJ, Stein R, Thiene G, Zeppilli P, McKenna WJ. Section of sports cardiology EAoCP and rehabilitation. Recommendations for interpretation of 12‐lead electrocardiogram in the athlete. Eur Heart J 31: 243‐259, 2010.
 16.Dauvin A, Donado C, Bachtiger P, Huang K‐C, Sauer CM, Ramazzotti D, Bonvini M, Celi LA, Douglas MJ. Machine learning can accurately predict pre‐admission baseline hemoglobin and creatinine in intensive care patients. Npj Digit Med 2:116, 2019.
 17.Diener HC, Sacco RL, Easton JD, Granger CB, Bernstein RA, Uchiyama S, Kreuzer J, Cronin L, Cotton D, Grauer C, Brueckmann M, Chernyatina M, Donnan G, Ferro JM, Grond M, Kallmünzer B, Krupinski J, Lee BC, Lemmens R, Masjuan J, Odinak M, Saver JL, Schellinger PD, Toni D, Toyoda K, RE‐SPECT ESUS Steering Committee and Investigators. Dabigatran for prevention of stroke after embolic stroke of undetermined source. N Engl J Med 380 (20): 1906‐1917, 2019.
 18.Dilaveris PE, Gialafos EJ, Andrikopoulos GK, Richter DJ, Papanikolaou V, Poralis K, Gialafos JE. Clinical and electrocardiographic predictors of recurrent atrial fibrillation. Pacing Clin Electrophysiol 23 (2): 852‐858, 2000.
 19.Elansary M, Hamdi M, Zaghla H, Ragab D. P‐wave dispersion and left atrial indices as predictors of paroxysmal atrial fibrillation in patients with non hemorrhagic cerebrovascular strokes and transient ischemic attacks. Egypt HearJ 66 (4): 369‐374, 2014.
 20.Farid F, Elkhodr M, Sabrina F, Ahamed F, Gide E. A smart biometric identity management framework for personalize IoT and cloud computing‐based healthcare services. Sensors (Basel) 21 (2): 552, 2021.
 21.Finocchiaro G, Sheikh N, Biagini E, Papadakis M, Maurizi N, Sinagra G, Pelliccia A, Rapezzi C, Sharma S, Olivotto I. The electrocardiogram in the diagnosis and management of patients with hypertrophic cardiomyopathy. Heart Rhythm 17: 142‐151, 2020.
 22.Fu Z, Hong S, Zhang R, Du S. Artificial‐intelligence‐enabled mobile system for cardiovascular health management. Sensors (Basel) 21 (3): 773, 2021.
 23.Galloway CD, Valys AV, Sheribati JB, Treiman DL, Petterson FL, Gundotra VP, Albert DE, Attia ZI, Carter RE, Asirvatham SJ, Ackerman MJ, Noseworthy PA, Dillon JJ, Friedman PA. Development and validation of a deep‐learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol 4 (5): 428‐436, 2019.
 24.Gladstone DJ, Spring M, Dorian P, Panzov V, Thorpe KE, Hall J, Vaid H, O'Donnell M, Laupacis A, Côté R, Sharma M, Blakely JA, Shuaib A, Hachinski V, Coutts SB, Sahlas DJ, Teal P, Yip S, Spence JD, Buck B, Verreault S, Casaubon LK, Penn A, Selchen D, Jin A, Howse D, Mehdiratta M, Boyle K, Aviv R, Kapral MK, Mamdani M, EMBRACE Investigators and Coordinators. Atrial fibrillation in patients with cryptogenic stroke. N Engl J Med 370: 2467‐2477, 2014.
 25.Hajirasouliha I, Elemento O. Precision medicine and artificial intelligence: Overview and relevance to reproductive medicine. Fertil Steril 114 (5): 908‐913, 2020.
 26.Hart RG, Sharma M, Mundl H, Kasner SE, Bangdiwala SI, Berkowitz SD, Swaminathan B, Lavados P, Wang Y, Wang Y, Davalos A, Shamalov N, Mikulik R, Cunha L, Lindgren A, Arauz A, Lang W, Czlonkowska A, Eckstein J, Gagliardi RJ, Amarenco P, Ameriso SF, Tatlisumak T, Veltkamp R, Hankey GJ, Toni D, Bereczki D, Uchiyama S, Ntaios G, Yoon BW, Brouns R, Endres M, Muir KW, Bornstein N, Ozturk S, O'Donnell MJ, De Vries Basson MM, Pare G, Pater C, Kirsch B, Sheridan P, Peters G, Weitz JI, Peacock WF, Shoamanesh A, Benavente OR, Joyner C, Themeles E, Connolly SJ, NAVIGATE ESUS Investigators. Rivaroxaban for stroke prevention after embolic stroke of undetermined source. N Engl J Med 378 (23): 2191‐2201, 2018.
 27.Jo Y‐Y, Cho Y, Lee SY, Kwon J‐M, Kim K‐H, Jeon K‐H, Cho S, Park J, Oh B‐H. Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram. Int J Cardiol 328: 104‐110, 2021.
 28.Ko WY, Siontis KC, Attia ZI, Carter RE, Kapa S, Ommen SR, Demuth SJ, Ackerman MJ, Gersh BJ, Arruda‐Olson AM, Geske JB, Asirvatham SJ, Lopez‐Jimenez F, Nishimura RA, Friedman PA, Noseworthy PA. Detection of hypertrophic cardiomyopathy using a convolutional neural network‐enabled electrocardiogram. J Am Coll Cardiol 75: 722‐733, 2020.
 29.Kwon J‐M, Jung M‐S, Kim K‐H, Jo Y‐Y, Shin J‐H, Cho Y‐H, Lee Y‐J, Ban J‐H, Jeon K‐H, Lee SY, Park J, Oh B‐H. Artificial intelligence for detecting electrolyte imbalance using electrocardiography. Ann Noninvasive Electrocardiol 26: e12839, 2021.
 30.Li L, Yiin GS, Geraghty OC, Schulz UG, Kuker W, Mehta Z, Rothwell PM, Oxford Vascular Study. Incidence, outcome, risk factors, and long‐term prognosis of cryptogenic transient ischaemic attack and ischaemic stroke: A population‐based study. Lancet Neurol 14 (9): 903‐913, 2015.
 31.Lima EM, Ribeiro AH, Paixao GMM, Ribeiro MH, Pinto‐Filho MM, Gomes PR, Oliveira DM, Sabino EC, Duncan BB, Giatti L, Barreto SM, Meira W Jr, Schon TB, Ribeiro ALP. Deep neural network‐estimated electrocardiographic age as a mortality predictor. Nat Commun 12: 5117, 2021.
 32.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 SJ, Lin S‐H. A deep‐learning algorithm (ECG12Net) for detecting hypokalemia and hyperkalemia by electrocardiography: Algorithm development. JMIR Med Inform 8: e15931, 2020.
 33.Marinucci D, Sbrollini A, Marcantoni I, Morettini M, Swenne CA, Burattini L. Artificial neural network for atrial fibrillation identification in portable devices. Sensors (Basel) 20: 3570, 2020.
 34.Maron BJ, Gardin JM, Flack JM, Gidding SS, Kurosaki TT, Bild DE. Prevalence of hypertrophic cardiomyopathy in a general population of young adults. Echocardiographic analysis of 4111 subjects in the CARDIA Study. Coronary Artery Risk Development in (Young) Adults. Circulation 92: 785‐789, 1995.
 35.Martinez‐Selles M, Masso‐van Roessel A, Alvarez‐Garcia J, García de la Villa B, Cruz‐Jentoft AJ, Vidán MT, López Díaz J, Felix Redondo FJ, Durán Guerrero JM, Bayes‐Genis A, Bayes de Luna A, Investigators of the Cardiac and Clinical Characterization of Centenarians (4C) registry. Interatrial block and atrial arrhythmias in centenarians: Prevalence, associations, and clinical implications. Heart Rhythm 13: 645‐651, 2016.
 36.McLeod CJ, Ackerman MJ, Nishimura RA, Tajik AJ, Gersh BJ, Ommen SR. Outcome of patients with hypertrophic cardiomyopathy and a normal electrocardiogram. J Am Coll Cardiol 54: 229‐233, 2009.
 37.Noordam R, Young WJ, Salman R, Kanters JK, van den Berg ME, van Heemst D, Lin HJ, Barreto SM, Biggs ML, Biino G, Catamo E, Concas MP, Ding J, Evans DS, Foco L, Grarup N, Lyytikäinen LP, Mangino M, Mei H, van der Most PJ, Müller‐Nurasyid M, Nelson CP, Qian Y, Repetto L, Said MA, Shah N, Schramm K, Vidigal PG, Weiss S, Yao J, Zilhao NR, Brody JA, Braund PS, Brumat M, Campana E, Christofidou P, Caulfield MJ, De Grandi A, Dominiczak AF, Doney ASF, Eiriksdottir G, Ellervik C, Giatti L, Gögele M, Graff C, Guo X, van der Harst P, Joshi PK, Kähönen M, Kestenbaum B, Lima‐Costa MF, Linneberg A, Maan AC, Meitinger T, Padmanabhan S, Pattaro C, Peters A, Petersmann A, Sever P, Sinner MF, Shen X, Stanton A, Strauch K, Soliman EZ, Tarasov KV, Taylor KD, Thio CHL, Uitterlinden AG, Vaccargiu S, Waldenberger M, Robino A, Correa A, Cucca F, Cummings SR, Dörr M, Girotto G, Gudnason V, Hansen T, Heckbert SR, Juhl CR, Kääb S, Lehtimäki T, Liu Y, Lotufo PA, Palmer CNA, Pirastu M, Pramstaller PP, Ribeiro ALP, Rotter JI, Samani NJ, Snieder H, Spector TD, Stricker BH, Verweij N, Wilson JF, Wilson JG, Jukema JW, Tinker A, Newton‐Cheh CH, Sotoodehnia N, Mook‐Kanamori DO, Munroe PB, Warren HR. Effects of calcium, magnesium, and potassium concentrations on ventricular repolarization in unselected individuals. J Am Coll Cardiol 73 (24): 3118‐3131, 2019.
 38.Ommen SR, Mital S, Burke MA, Day SM, Deswal A, Elliott P, Evanovich LL, Hung J, Joglar JA, Kantor P, Kimmelstiel C, Kittleson M, Link MS, Maron MS, Martinez MW, Miyake CY, Schaff HV, Semsarian C, Sorajja P. AHA/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2020: CIR0000000000000937, 2020.
 39.Panza JA, Maron BJ. Relation of electrocardiographic abnormalities to evolving left ventricular hypertrophy in hypertrophic cardiomyopathy during childhood. Am J Cardiol 63: 1258‐1265, 1989.
 40.Rapezzi C, Arbustini E, Caforio AL, Charron P, Gimeno‐Blanes J, Helio T, Linhart A, Mogensen J, Pinto Y, Ristic A, Seggewiss H, Sinagra G, Tavazzi L, Elliott PM. Diagnostic work‐up in cardiomyopathies: Bridging the gap between clinical phenotypes and final diagnosis. A position statement from the ESC Working Group on Myocardial and Pericardial Diseases. Eur Heart J 34: 1448‐1458, 2013.
 41.Sahay S, Nombela‐Franco L, Rodes‐Cabau J, Jimenez‐Quevedo P, Salinas P, Biagioni C, Nuñez‐Gil I, Gonzalo N, de Agustín JA, Del Trigo M, Perez de Isla L, Fernández‐Ortiz A, Escaned J, Macaya C. Efficacy and safety of left atrial appendage closure versus medical treatment in atrial fibrillation: A network meta‐analysis from randomized trials. Heart 103 (2): 139‐147, 2017.
 42.Sanna T, Diener HC, Passman RS, Di Lazzaro V, Bernstein RA, Morillo CA, Rymer MM, Thijs V, Rogers T, Beckers F, Lindborg K, Brachmann J, CRYSTAL AF Investigators. Cryptogenic stroke and underlying atrial fibrillation. N Engl J Med 370: 2478‐2486, 2014.
 43.Semsarian C, Ingles J, Maron MS, Maron BJ. New perspectives on the prevalence of hypertrophic cardiomyopathy. J Am Coll Cardiol 65: 1249‐1254, 2015.
 44.Sharma S, Merghani A, Mont L. Exercise and the heart: The good, the bad, and the ugly. Eur Heart J 36: 1445‐1453, 2015.
 45.Siontis KC, Liu K, Bos JM, Attia ZI, Cohen‐Shelly M, Arruda‐Olson AM, Farahani NZ, Friedman PA, Noseworthy PA, Ackerman MJ. Detection of hypertrophic cardiomyopathy by artificial intelligence electrocardiogram in children and adolescents. Int J Cardiol 340: 42‐47, 2021.
 46.Taniguchi H, Takata T, Takechi M, Furukawa A, Iwasawa J, Kawamura A, Taniguchi T, Tamura Y. Explainable artificial intelligence for diagnosis of atrial fibrillation using Holter electrocardiogram waveforms. Int Heart J 62: 534‐539, 2021.
 47.Tison GH, Zhang J, Delling FN, Deo RC. Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery. Circ Cardiovasc Qual Outcomes 12: e005289, 2019.
 48.Uberoi A, Stein R, Perez MV, Freeman J, Wheeler M, Dewey F, Peidro R, Hadley D, Drezner J, Sharma S, Pelliccia A, Corrado D, Niebauer J, Estes NA 3rd, Ashley E, Froelicher V. Interpretation of the electrocardiogram of young athletes. Circulation 124: 746‐757, 2011.
 49.Wang C‐X, Zhang Y‐C, Kong Q‐L, Wu Z‐X, Yang P‐P, Zhu C‐H, Chen S‐L, Wu T, Wu Q‐H, Chen Q. Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients. Chin Med J (Engl), 2021. DOI: 10.1097/CM9.0000000000001650.
 50.Wang ZC, Li L, Glicksberg BS, Israel A, Dudley JT, Ma'ayan A. Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age. J Biomed Inform 76: 59‐68, 2017.
 51.Williams AM, Liu Y, Regner KR, Jotterand F, Liu P, Liang M. Artificial intelligence, physiological genomics, and precision medicine. Physiol Genomics 50 (4): 237‐243, 2018.
 52.Yoshizawa T, Niwano S, Niwano H, Igarashi T, Fujiishi T, Ishizue N, Oikawa J, Satoh A, Kurokawa S, Hatakeyama Y, Fukaya H, Ako J. Prediction of new onset atrial fibrillation through P wave analysis in 12 lead ECG. Int Heart J 55 (5): 422‐427, 2014.

Contact Editor

Submit a note to the editor about this article by filling in the form below.

* Required Field

How to Cite

Anthony H. Kashou, Demilade A. Adedinsewo, Konstantinos C. Siontis, Peter A. Noseworthy. Artificial Intelligence‐Enabled ECG: Physiologic and Pathophysiologic Insights and Implications. Compr Physiol 2022, 12: 3417-3424. doi: 10.1002/cphy.c210001