{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T17:11:32Z","timestamp":1772557892325,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) are common cardiomyopathies associated with heart failure. Electrocardiogram (ECG) screening before an echocardiogram could help streamline diagnosis, particularly in rural areas. Prior ECG\u2013machine learning (ML) studies do not use open-source data when studying cardiomyopathy, and very few proprietary studies directly compare HCM and DCM or address ECG differences within obstructive (HOCM) and non-obstructive HCM (HNCM). Methods: Standard and vectorcardiogram-derived (VCG) ECG features were extracted from the MIMIC-IV-ECG database. The final cohort comprised 599 patients (HCM = 208 [HOCM = 99, HNCM = 53, unknown = 56]; DCM = 391 [ischemic cardiomyopathy with left ventricular dilation = 250, non-ischemic = 141]). Logistic regression (LR) and extreme gradient boosting (XGBoost) with five-fold cross-validation separated HCM from ischemic cardiomyopathy with left ventricular dilation (DCM-I) and non-ischemic DCM (DCM-NI), and HOCM from HNCM. Results: Using the area under the receiver-operating-characteristic curve (AUC-ROC) as the performance metric, LR achieved high discrimination of HCM from DCM-I (0.92) and DCM-NI (0.90). However, differentiating HOCM from HNCM proved more difficult (XGBoost = 0.81; LR = 0.75). Both DCM subtypes (especially ischemic) showed lower QRS amplitudes and right-posterior ventricular gradient orientation; HCM displayed higher amplitudes and larger, more complex T-loops. Within HCM, HOCM had stronger leftward electrical activity and more dipolar to non-dipolar QRS energy after singular value decomposition. Conclusions: Using only open-access data, we demonstrate an interpretable ECG-based pipeline that discriminates cardiomyopathy and highlights distinct features. While detecting obstruction remains difficult, ECG features provide measurable separation, supporting possible diagnostic screening and offering a reproducible framework for future studies.<\/jats:p>","DOI":"10.3390\/diagnostics16050719","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:22:21Z","timestamp":1772551341000},"page":"719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Open-Source Analysis of Cardiomyopathy Using Machine Learning and Electrocardiograms"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2367-9920","authenticated-orcid":false,"given":"Arda","family":"Altintepe","sequence":"first","affiliation":[{"name":"Horace Mann School, Bronx, NY 10471, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asu","family":"Rustemli","sequence":"additional","affiliation":[{"name":"Ocean Cardiovascular, LLC, Toms River, NJ 08755, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0777-305X","authenticated-orcid":false,"given":"Amir Reza","family":"Vazifeh","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA"},{"name":"Princeton Precision Health, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason W.","family":"Fleischer","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA"},{"name":"Princeton Precision Health, Princeton University, Princeton, NJ 08544, USA"},{"name":"Omenn-Darling Bioengineering Institute, Princeton University, Bioengineering Building, 35 Ivy Lane, Princeton, NJ 08540, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ciarambino, T., Menna, G., Sansone, G., and Giordano, M. (2021). Cardiomyopathies: An overview. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22147722"},{"key":"ref_2","unstructured":"Ashley, E.A., and Niebauer, J. (2004). Cardiology Explained, Remedica."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2123","DOI":"10.1016\/j.jcmg.2021.03.032","article-title":"Maximal wall thickness measurement in hypertrophic cardiomyopathy: Biomarker variability and its impact on clinical care","volume":"14","author":"Captur","year":"2021","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1056\/NEJMoa021332","article-title":"Effect of left ventricular outflow tract obstruction on clinical outcome in hypertrophic cardiomyopathy","volume":"348","author":"Maron","year":"2003","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nasimov, R., Nasimova, N., Botirjon, K., and Abdullayev, M. (2023). Deep-Learning Algorithm for Classifying Dilated and Hypertrophic Cardiomyopathy in Transport Workers. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-031-30258-9_19"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhou, M., Deng, Y., Liu, Y., Su, X., and Zeng, X. (2023). Echocardiography-based machine-learning algorithm for distinguishing ischaemic from dilated cardiomyopathy. BMC Cardiovasc. Disord., 23.","DOI":"10.1186\/s12872-023-03520-4"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1016\/j.jacc.2019.12.030","article-title":"Detection of hypertrophic cardiomyopathy with a convolutional-neural-network-enabled electrocardiogram","volume":"75","author":"Ko","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1536\/ihj.22-132","article-title":"Automatic detection of left-ventricular dilatation and hypertrophy from electrocardiograms using deep learning","volume":"63","author":"Kokubo","year":"2022","journal-title":"Int. Heart J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bacharova, L., Kollarova, M., Bezak, B., and Bohm, A. (2023). Left-ventricular hypertrophy and ventricular tachyarrhythmia: The role of biomarkers. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24043881"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"euae093","DOI":"10.1093\/europace\/euae093","article-title":"Accurate diagnosis of apical hypertrophic cardiomyopathy using explainable advanced electrocardiogram analysis","volume":"26","author":"Hughes","year":"2024","journal-title":"Europace"},{"key":"ref_11","first-page":"46","article-title":"Role of surface electrocardiography in differentiation between obstructive and non-obstructive hypertrophic cardiomyopathy","volume":"18","author":"Nourani","year":"2023","journal-title":"J. Tehran Heart Cent."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"40","DOI":"10.5334\/gh.1250","article-title":"Identifying Obstructive Hypertrophic Cardiomyopathy from Nonobstructive Hypertrophic Cardiomyopathy: Development and Validation of a Model Based on Electrocardiogram Features","volume":"18","author":"Guo","year":"2023","journal-title":"Glob. Heart"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health-record database","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci. Data"},{"key":"ref_14","unstructured":"Johnson, A.E.W., Bulgarelli, L., Pollard, T.J., Gow, B., Moody, B., Horng, S., Celi, L.A., and Mark, R. (2024). MIMIC-IV (Version 3.0), PhysioNet."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_16","unstructured":"Gow, B., Pollard, T., Nathanson, L.A., Johnson, A., Moody, B., Fernandes, C., Greenbaum, N., Waks, J.W., Eslami, P., and Carbonati, T. (2023). MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset (Version 1.0), PhysioNet."},{"key":"ref_17","unstructured":"Johnson, A.E.W., Pollard, T., Horng, S., Celi, L.A., and Mark, R. (2023). MIMIC-IV-Note: Deidentified Free-Text Clinical Notes (Version 2.2), PhysioNet."},{"key":"ref_18","unstructured":"OpenAI (2025, April 09). GPT-4.1 (Large Language Model) [Computer Software]. Available online: https:\/\/platform.openai.com\/."},{"key":"ref_19","unstructured":"Xie, C., McCullum, L., Johnson, A., Pollard, T., Gow, B., and Moody, B. (2023). Waveform Database Software Package (WFDB) for Python (Version 4.1.0), PhysioNet."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","article-title":"NeuroKit2: A Python toolbox for neurophysiological signal processing","volume":"53","author":"Makowski","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Emrich, J., Gargano, A., Koka, T., and Muma, M. (2024, January 26\u201330). Physiology-Informed ECG Delineation Based on Peak Prominence. Proceedings of the 32nd European Signal Processing Conference (EUSIPCO), Lyon, France.","DOI":"10.23919\/EUSIPCO63174.2024.10715353"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1093\/oxfordjournals.eurheartj.a059647","article-title":"Reconstruction of the Frank vectorcardiogram from standard electrocardiographic leads: Diagnostic comparison of different methods","volume":"11","author":"Kors","year":"1990","journal-title":"Eur. Heart J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.ejheart.2007.12.012","article-title":"Gender-related differences in patients presenting with acute heart failure: EuroHeart Failure Survey II","volume":"10","author":"Nieminen","year":"2008","journal-title":"Eur. J. Heart Fail."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"C179","DOI":"10.1093\/eurheartjsupp\/suad043","article-title":"The electrocardiogram in non-ischaemic dilated cardiomyopathy","volume":"25","author":"Crescenzi","year":"2023","journal-title":"Eur. Heart J. Suppl."},{"key":"ref_25","first-page":"106","article-title":"Poor R-wave progression and myocardial infarct size after anterior myocardial infarction in the coronary intervention era","volume":"7","author":"Kurisu","year":"2015","journal-title":"Int. J. Cardiol. Heart Vasc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s12471-011-0162-9","article-title":"Precordial R-wave reappearance predicting infarct size and myocardial recovery after acute STEMI","volume":"20","author":"Rijnierse","year":"2012","journal-title":"Neth. Heart J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1161\/01.CIR.58.3.402","article-title":"Electrocardiographic findings in patients with obstructive and nonobstructive hypertrophic cardiomyopathy","volume":"58","author":"Savage","year":"1978","journal-title":"Circulation"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/719\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:44:05Z","timestamp":1772552645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/5\/719"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,28]]},"references-count":27,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["diagnostics16050719"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16050719","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,28]]}}}