Research Article|Articles in Press

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning



      Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes. We asked: does an automated deep learning approach to ECG interpretation detect PH and its clinically important subtypes?

      Methods and Results

      Adults with right heart catheterization or an echocardiogram within 90 days of an ECG at the University of California, San Francisco (2012–2019) were retrospectively identified as PH or non-PH. A deep convolutional neural network was trained on patients’ 12-lead ECG voltage data. Patients were divided into training, development, and test sets in a ratio of 7:1:2. Overall, 5016 PH and 19,454 patients without PH were used in the study. The mean age at the time of ECG was 62.29 ± 17.58 years and 49.88% were female. The mean interval between ECG and right heart catheterization or echocardiogram was 3.66 and 2.23 days for patients with PH and patients without PH, respectively. In the test dataset, the model achieved an area under the receiver operating characteristic curve, sensitivity, and specificity, respectively of 0.89, 0.79, and 0.84 to detect PH; 0.91, 0.83, and 0.84 to detect precapillary PH; 0.88, 0.81, and 0.81 to detect pulmonary arterial hypertension, and 0.80, 0.73, and 0.76 to detect group 3 PH. We additionally applied the trained model on ECGs from participants in the test dataset that were obtained from up to 2 years before diagnosis of PH; the area under the receiver operating characteristic curve was 0.79 or greater.


      A deep learning ECG algorithm can detect PH and PH subtypes around the time of diagnosis and can detect PH using ECGs that were done up to 2 years before right heart catheterization/echocardiogram diagnosis. This approach has the potential to decrease diagnostic delays in PH.

      Key Words

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        • Simonneau G
        • Montani D
        • Celermajer DS
        • Denton CP
        • Gatzoulis MA
        • Krowka M
        • et al.
        Haemodynamic definitions and updated clinical classification of pulmonary hypertension.
        Eur Respir J. 2019; 531801913
        • Benza RL
        • Miller DP
        • Barst RJ
        • Badesch DB
        • Frost AE
        • McGoon MD
        An evaluation of long-term survival from time of diagnosis in pulmonary arterial hypertension from the REVEAL Registry.
        Chest. 2012; 142: 448-456
        • Gall H
        • Felix JF
        • Schneck FK
        • Milger K
        • Sommer N
        • Voswinckel R
        • et al.
        The Giessen Pulmonary Hypertension Registry: Survival in pulmonary hypertension subgroups.
        J Heart Lung Transplant. 2017; 36: 957-967
        • Khou V
        • Anderson JJ
        • Strange G
        • Corrigan C
        • Collins N
        • Celermajer DS
        • et al.
        Diagnostic delay in pulmonary arterial hypertension: Insights from the Australian and New Zealand pulmonary hypertension registry.
        Respirology. 2020; 25: 863-871
        • Strange G
        • Gabbay E
        • Kermeen F
        • Williams T
        • Carrington M
        • Stewart S
        • et al.
        Time from symptoms to definitive diagnosis of idiopathic pulmonary arterial hypertension: the delay study.
        Pulmonary Circ. 2013; 3: 89-94
        • Galiè N
        • Humbert M
        • Vachiery J-L
        • Gibbs S
        • Lang I
        • Torbicki A
        • et al.
        2015 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension. The Joint Task Force for the Diagnosis and Treatment of Pulmonary Hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS) endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC).
        Eur Respir J. 2015; 46: 903-975
        • Kim MH
        • Johnston SS
        • Chu BC
        • Dalal MR
        • Schulman KL
        Estimation of total incremental health care costs in patients with atrial fibrillation in the United States.
        Circ Cardiovasc Qual Outcomes. 2011; 4: 313-320
        • Wang TJ
        • Larson MG
        • Levy D
        • Vasan RS
        • Leip EP
        • Wolf PA
        • et al.
        Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study.
        Circulation. 2003; 107: 2920-2925
        • Armstrong I
        • Billings C
        • Kiely DG
        • Yorke J
        • Harries C
        • Clayton S
        • et al.
        The patient experience of pulmonary hypertension: a large cross-sectional study of UK patients.
        BMC Pulmonary Med. 2019; 19: 67
        • Brown LM
        • Chen H
        • Halpern S
        • Taichman D
        • McGoon MD
        • Farber HW
        • et al.
        Delay in recognition of pulmonary arterial hypertension: factors identified from the REVEAL Registry.
        Chest. 2011; 140: 19-26
        • Kiely DG
        • Lawrie A
        • Humbert M
        Screening strategies for pulmonary arterial hypertension.
        Eur Heart J Suppl. 2019; 21: K9-K20
        • Ollivier C
        • Sun H
        • Amchin W
        • Beghetti M
        • Berger RMF
        • Breitenstein S
        • et al.
        New strategies for the conduct of clinical trials in pediatric pulmonary arterial hypertension: outcome of a multistakeholder meeting with patients, academia, industry, and regulators, held at the European Medicines Agency on Monday, June 12, 2017.
        J Am Heart Assoc. 2019; 8e011306
        • Sobczyk D
        • Nycz K
        • Andruszkiewicz P
        Validity of a 5-minute focused echocardiography with A-F mnemonic performed by non-echocardiographers in the management of patients with acute chest pain.
        Cardiovasc Ultrasound. 2015; 13: 16
      1. Rui P, Okeyode T. National Ambulatory Medical Care Survey: 2016 national summary tables. Available from: 2016.

        • Bhatia RS
        • Bouck Z
        • Ivers NM
        • Mecredy G
        • Singh J
        • Pendrith C
        • et al.
        Electrocardiograms in low-risk patients undergoing an annual health examination.
        JAMA Intern Med. 2017; 177: 1326-1333
        • Pitts SR
        • Niska RW
        • Xu J
        • Burt CW
        National Hospital Ambulatory Medical Care Survey: 2006 emergency department summary.
        Natl Health Stat Report. 2008; 6: 1-38
        • Bossone E
        • Paciocco G
        • Iarussi D
        • Agretto A
        • Iacono A
        • Gillespie BW
        • et al.
        The prognostic role of ECG in primary pulmonary hypertension.
        Chest. 2002; 121: 513-518
        • Tison GH
        • Zhang J
        • Delling FN
        • Deo RC
        Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery.
        Circ Cardiovasc Qual Outcomes. 2019; 12e005289
        • He K
        • Zhang X
        • Ren S
        • Sun J
        Deep residual learning for image recognition.
        in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016: 770-778
        • Hannun AY
        • Rajpurkar P
        • Haghpanahi M
        • Tison GH
        • Bourn C
        • Turakhia MP
        • et al.
        Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.
        Nat Med. 2019; 25: 65-69
        • Nair V
        • Hinton GE
        Rectified linear units improve restricted Boltzmann machines.
        in: Paper presented at: Proceedings of the 27th International Conference on International Machine Learning, Haifa, Israel2010
        • Ioffe S
        • Szegedy C
        Batch normalization: accelerating deep network training by reducing internal covariate shift.
        Proceedings of the 32nd International Conference on Machine Learning. 2015; 37: 448-456
        • Kingma DP
        • Ba JL
        Adam: a method for stochastic optimization.
        in: 3rd International Conference on Learning Representations. ICLR, 2015: 1-15
        • Ribeiro MT
        • Singh S
        • Guestrin C
        Why should I trust you?” explaining the predictions of any classifier.
        in: KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 1135-1144
        • Saito T
        • Rehmsmeier M
        The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.
        PLoS One. 2015; 10e0118432
        • Hoeper MM
        • Humbert M
        • Souza R
        • Idrees M
        • Kawut SM
        • Sliwa-Hahnle K
        • et al.
        A global view of pulmonary hypertension.
        Lancet Respir Med. 2016; 4: 306-322
        • Parikh R
        • Mathai A
        • Parikh S
        • Chandra Sekhar G
        • Thomas R
        Understanding and using sensitivity, specificity and predictive values.
        Indian J Ophthalmol. 2008; 56: 45-50
        • Chazova IY
        • Martynyuk TV
        • Valieva ZS
        • Gratsianskaya SY
        • Aleevskaya AM
        • Zorin AV
        • et al.
        Clinical and instrumental characteristics of newly diagnosed patients with various forms of pulmonary hypertension according to the Russian National Registry.
        BioMed Res Int. 2020; 20206836973
        • Escribano-Subias P
        • Blanco I
        • López-Meseguer M
        • Lopez-Guarch CJ
        • Roman A
        • Morales P
        • et al.
        Survival in pulmonary hypertension in Spain: insights from the Spanish registry.
        Eur Respir J. 2012; 40: 596-603
        • Kwon J-m
        • Kim K-H
        • Medina-Inojosa J
        • Jeon K-H
        • Park J
        • Oh B-H
        Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography.
        Journal Heart Lung Transplant. 2020; 39: 805-814
        • Ni JR
        • Yan PJ
        • Liu SD
        • Hu Y
        • Yang KH
        • Song B
        • et al.
        Diagnostic accuracy of transthoracic echocardiography for pulmonary hypertension: a systematic review and meta-analysis.
        BMJ Open. 2019; 9e033084
        • Choi E
        • Brown RE
        • Sullivan MJ
        • Andrus BW
        Echocardiography reporting of pulmonary hypertension and subsequent referral to a specialty clinic.
        Echocardiography. 2020; 37: 8-13
        • Kanwar MK
        • Tedford RJ
        • Thenappan T
        • Marco TD
        • Park M
        • McLaughlin V
        Elevated pulmonary pressure noted on echocardiogram: a simplified approach to next steps.
        J Am Heart Assoc. 2021; 10e017684
        • O'Leary JM
        • Assad TR
        • Xu M
        • Farber-Eger E
        • Wells QS
        • Hemnes AR
        • et al.
        Lack of a tricuspid regurgitation doppler signal and pulmonary hypertension by invasive measurement.
        J Am Heart Assoc. 2018; 7e009111
        • Munt B
        • O'Neill BJ
        • Koilpillai C
        • Gin K
        • Jue J
        • Honos G
        Treating the right patient at the right time: access to echocardiography in Canada.
        Can J Cardiol. 2006; 22: 1029-1033
        • Papolos A
        • Narula J
        • Bavishi C
        • Chaudhry FA
        • Sengupta PP
        U.S. Hospital use of echocardiography: insights from the Nationwide Inpatient Sample.
        J Am Coll Cardiol. 2016; 67: 502-511
        • van Gurp N
        • Boonman-De Winter LJ
        • Meijer Timmerman Thijssen DW
        • Stoffers HE
        Benefits of an open access echocardiography service: a Dutch prospective cohort study.
        Netherlands Heart J. 2013; 21: 399-405
        • Siontis KC
        • Noseworthy PA
        • Attia ZI
        • Friedman PA
        Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.
        Nat Rev Cardiol. 2021; 18: 465-478
        • Hughes JW
        • Olgin JE
        • Avram R
        • Abreau SA
        • Sittler T
        • Radia K
        • et al.
        Performance of a convolutional neural network and explainability technique for 12-lead electrocardiogram interpretation.
        JAMA Cardiol. 2021; 6: 1285-1295
        • Tison GH
        • Siontis KC
        • Abreau S
        • Attia Z
        • Agarwal P
        • Balasubramanyam A
        • et al.
        PIONEER-OLE Investigators. Assessment of Disease Status and Treatment Response With Artificial Intelligence-Enhanced Electrocardiography in Obstructive Hypertrophic Cardiomyopathy.
        J Am Coll Cardiol. 2022; 79: 1032-1034
        • Ko WY
        • Siontis KC
        • Attia ZI
        • Carter RE
        • Kapa S
        • Ommen SR
        • et al.
        Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram.
        J Am Coll Cardiol. 2020; 75: 722-733
        • Attia ZI
        • Kapa S
        • Lopez-Jimenez F
        • McKie PM
        • Ladewig DJ
        • Satam G
        • et al.
        Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram.
        Nat Med. 2019; 25: 70-74
        • Yao X
        • Rushlow DR
        • Inselman JW
        • McCoy RG
        • Thacher TD
        • Behnken EM
        • et al.
        Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.
        Nat Med. 2021; 27: 815-819
        • Rocken C
        • Peters B
        • Juenemann G
        • Saeger W
        • Klein HU
        • Huth C
        • et al.
        Atrial amyloidosis: an arrhythmogenic substrate for persistent atrial fibrillation.
        Circulation. 2002; 106: 2091-2097
        • Mathai SC
        • Mathew S
        Breathing (and coding?) a bit easier: changes to International Classification of Disease coding for pulmonary hypertension.
        Chest. 2018; 154: 207-218
        • Noseworthy PA
        • Attia ZI
        • Brewer LC
        • Hayes SN
        • Yao X
        • Kapa S
        • et al.
        Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ECG analysis.
        Circ Arrhythmia Electrophysiol. 2020; 13e007988