CoChat
Sign Up
Cited by 82ECG Monitoring and AnalysisArtificial Intelligence in HealthcareCardiac pacing and defibrillation studies

Artificial Intelligence Assessment for Early Detection of Heart Failure with Preserved Ejection Fraction Based on Electrocardiographic Features

Open access
Kwon, Joon-myoung, Kim, Kyung-Hee, Eisen, Howard J, Cho, Younghoon, Jeon, Ki-Hyun, Lee, Soo Youn, Park, Jinsik, Oh, Byung-Hee·2021·European Heart Journal - Digital Health
DOI 10.1093/ehjdh/ztaa015PMC PMC9707919
Open this paper in CoChat — free accountOpen PDF

Abstract

Aims: Although heart failure with preserved ejection fraction (HFpEF) is a rapidly emerging global health problem, an adequate tool to screen it reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. Methods and results: This retrospective cohort study included two hospitals. 34 103 patients who underwent echocardiography and ECG within 1 week and indicated normal left ventricular systolic function were included in this study. A DLM based on an ensemble neural network was developed using 32 671 ECGs of 20 169 patients. The internal validation included 1979 ECGs of 1979 patients. Furthermore, we conducted an external validation with 11 955 ECGs of 11 955 patients from another hospital. The endpoint was to detect HFpEF. During the internal and external validation, the area under the receiver operating characteristic curves of a DLM using 12-lead ECG for detecting HFpEF were 0.866 (95% confidence interval 0.850–0.883) and 0.869 (0.860–0.877), respectively. In the 1412 individuals without HFpEF at initial echocardiography, patients whose DLM was defined as having a higher risk had a significantly higher chance of developing HFpEF than those in the low-risk group (33.6% vs. 8.4%, P < 0.001). Sensitivity map showed that the DLM focused on the QRS complex and T-wave. Conclusion: The DLM demonstrated high performance for HFpEF detection using not only a 12-lead ECG but also 6- single-lead ECG. These results suggest that HFpEF can be screened using conventional ECG devices and diverse life-type ECG machines employing the DLM, thereby preventing disease progression.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/ehjdh/ztaa015

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 30

  1. [1]Owan TE, Hodge DO, Herges RM, Jacobsen SJ, Roger VL, Redfield MM. Trends in prevalence and outcome of heart failure with preserved ejection fraction. N Engl J Med 2006; 355: 251– 259. 16855265
  2. [2]Reddy YNV, Borlaug BA. Heart failure with preserved ejection fraction. Curr Probl Cardiol 2016; 41: 145– 188. 26952248
  3. [3]Unger ED, Dubin RF, Deo R, Daruwalla V, Friedman JL, Medina C, Beussink L, Freed BH, Shah SJ. Association of chronic kidney disease with abnormal cardiac mechanics and adverse outcomes in patients with heart failure and preserved ejection fraction. Eur J Heart Fail 2016; 18: 103– 112. 26635076 PMC4713321
  4. [4]Meta-analysis Global Group in Chronic Heart Failure (MAGGIC). The survival of patients with heart failure with preserved or reduced left ventricular ejection fraction: an individual patient data meta-analysis. Eur Heart J 2012; 33: 1750– 1757. 21821849
  5. [5]Ponikowski P, Voors AA,, Anker SD, Bueno H, Cleland JGF, Coats AJS, Falk V, González-Juanatey JR, Harjola V-P, Jankowska EA, Jessup M, Linde C, Nihoyannopoulos P, Parissis JT, Pieske B, Riley JP, Rosano GMC, Ruilope LM, Ruschitzka F, Rutten FH, van der Meer P; ESC Scientific Document Group. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J 2016; 37: 2129– 2200. 27206819
  6. [6]Webb J, Fovargue L, Tøndel K, Porter B, Sieniewicz B, Gould J, Rinaldi CA, Ismail T, Chiribiri A, Carr-White G. The emerging role of cardiac magnetic resonance imaging in the evaluation of patients with HFpEF. Curr Heart Fail Rep 2018; 15: 1– 9. 29404975 PMC5811579
  7. [7]Dargie HJ. Effect of carvedilol on outcome after myocardial infarction in patients with left-ventricular dysfunction: the CAPRICORN randomised trial. Lancet (Lond, Engl) 2001; 357: 1385– 1390. 11356434
  8. [8]LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436– 444. 26017442
  9. [9]Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-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 2019;25: 70– 74. 30617318
  10. [10]Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 2019; 394: 861– 867. 31378392
  11. [12]Kwon J, Lee SY, Jeon K, Lee Y, Kim K, Park J, Oh B, Lee M. Deep learning-based algorithm for detecting aortic stenosis using electrocardiography. J Am Heart Assoc 2020; 9:e014717. doi: 10.1161/JAHA.119.014717. PMC7428650 32200712 · DOI · Paper page
  12. [13]Kwon J, Cho Y, Jeon K-H, Cho S, Kim K-H, Baek SD, Jeung S, Park J, Oh B-H. A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study. Lancet Digit Heal 2020; 2: e358– e367. 33328095
  13. [14]Zhang Y, Xia M. Application of deep neural network for congestive heart failure detection using ECG signals. J Phys Conf Ser 2020; 1642: 012021.
  14. [15]Samuel OW, Yang B, Geng Y, Asogbon MG, Pirbhulal S, Mzurikwao D, Idowu OP, Ogundele TJ, Li X, Chen S, Naik GR, Fang P, Han F, Li G. A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks. Futur Gener Comput Syst 2020; 110: 781– 794.
  15. [16]Sbrollini A, De Jongh MC, Ter Haar CC, Treskes RW, Man S, Burattini L, Swenne CA. Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach. Biomed Eng Online 2019; 18: 15. 30755195 PMC6371549
  16. [17]Nagueh SF, Smiseth OA, Appleton CP, Byrd BF, Dokainish H, Edvardsen T, Flachskampf FA, Gillebert TC, Klein AL, Lancellotti P, Marino P, Oh JK, Popescu BA, Waggoner AD. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr 2016; 29: 277– 314. 27037982
  17. [19]Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15: 1929– 1958.
  18. [20]Jayalakshmi T, Santhakumaran A. Statistical normalization and backpropagation for classification. Int J Comput Theory Eng 2011; 3: 89– 93.
  19. [21]Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X, Brain G. TensorFlow: a system for large-scale machine learning. 12th USENIX Symp Oper Syst Des Implement (OSDI ’16) 2016; pp. 265– 284.
  20. [22]Calcagno V, De Mazancourt C. glmulti: an R package for easy automated model selection with (generalized) linear models. J Stat Softw 2010; 34: 1– 29.
  21. [23]Khalilia M, Chakraborty S, Popescu M. Predicting disease risks from highly imbalanced data using random forest. BMC Med Inform Decis Making 2011; 11: 51. PMC3163175 21801360
  22. [24]Carpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med 2000; 19: 1141– 1164. 10797513
  23. [25]Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision; 2017. pp. 1; 618– 626.
  24. [26]Schisterman EF, Perkins NJ, Liu A, Bondell H. Optimal cut-point and its corresponding Youden index to discriminate individuals using pooled blood samples. Epidemiology 2005; 16: 73– 81. 15613948
  25. [27]Tan ES, Chan SP, Xu CF, Yap J, Richards AM, Ling LH, Sim D, Jaufeerally F, Yeo D, Loh SY, Ong HY, Leong KTG, Ng TP, Nyunt SZ, Feng L, Okin P, Lam CS, Lim TW. Cornell product is an ECG marker of heart failure with preserved ejection fraction. Heart Asia 2019; 11: e011108. 31244913 PMC6560919
  26. [28]Hendry PB, Krisdinarti L, Erika M. Scoring system based on electrocardiogram features to predict the type of heart failure in patients with chronic heart failure. Cardiol Res 2016; 7: 110– 116. 28197277 PMC5295517
  27. [29]Lund LH, Jurga J, Edner M, Benson L, Dahlström U, Linde C, Alehagen U. Prevalence, correlates, and prognostic significance of QRS prolongation in heart failure with reduced and preserved ejection fraction. Eur Heart J 2013; 34: 529– 539. 23041499
  28. [30]Kagiyama N, Piccirilli M, Yanamala N, Shrestha S, Farjo PD, Casaclang-Verzosa G, Tarhuni WM, Nezarat N, Budoff MJ, Narula J, Sengupta PP. Machine learning assessment of left ventricular diastolic function based on electrocardiographic features. J Am Coll Cardiol 2020; 76: 930– 941. 32819467
  29. [31]Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S, Conant EF, Fajardo LL, Bassett L, D’Orsi C, Jong R, Rebner M; Digital Mammographic Imaging Screening Trial (DMIST) Investigators Group. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med 2005; 353: 1773– 1783. 16169887
  30. [32]Haug U, Kuntz KM, Knudsen AB, Hundt S, Brenner H. Sensitivity of immunochemical faecal occult blood testing for detecting left- vs right-sided colorectal neoplasia. Br J Cancer 2011; 104: 1779– 1785. 21559011 PMC3111170

Cited by

Sorted by citations

Authors
Kwon, J.-M., Kim, K.-H., Eisen, H. J., Cho, Y., Jeon, K.-H., Lee, S. Y., Park, J., & Oh, B.-H.
Affiliations
  • Sejong Institute (KR)
  • Pennsylvania State University (US)
  • Medical R&D Center, Body Friend, Co . Seoul, South Korea
  • Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital , Incheon, South Korea
Corresponding author
Kim, Kyung-Hee
ORCID
Venue
European Heart Journal - Digital Health
Publisher
Oxford University Press (OUP)
Published
2020-12-10
Volume
2
Issue
1
Pages
106-116
ISSN
2634-3916 (ISSN-L)
DOI
10.1093/ehjdh/ztaa015
PMC
PMC9707919
OpenAlex
W3112817906
Cited by
82
References
2
Citations by year
2026: 24 · 2025: 20 · 2024: 14 · 2023: 11 · 2022: 5 · 2021: 8
FWCI
3.71
Type
journal article
Language
en
Open access
Licence
Locations
  • European Heart Journal - Digital Health (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • National Research Foundation of Korea
  • NRF
  • Korea government (2020R1F1A1073791)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac pacing and defibrillation studies
Keywords
heart failure with preserved ejection fraction, electrocardiogram, early detection, explainable deep learning, neural network ensembles, 12-lead ECG, single-lead ECG, QRS complex, T wave, left ventricular diastolic dysfunction, disease screening, external validation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review