{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:42:58Z","timestamp":1773272578068,"version":"3.50.1"},"reference-count":41,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n Early diagnosis of Chagas disease plays a vital role in enabling timely treatment and reducing the likelihood of underlying severe cardiovascular complications. Electrocardiogram (ECG) signals contain vital information that reflects cardiac disease progression, motivating the use of advanced signal processing and machine intelligence for accurate diagnosis of Chagas disease. This work presents an automated system for Chagas disease detection using conventional 12-lead ECG recordings.\n Approach.<\/jats:italic>\n The method begins with preprocessing by standardizing the ECG sampling frequency and detecting QRS complexes. Then, four categories of features are extracted: (a) wavelet scattering transform coefficients from limb lead II and chest leads V1 and V3; (b) statistical descriptors from heart rate variability; (c) statistical features across all leads; and (d) patient metadata. The computed diagnostic feature vector is used as an input to the random under-sampling boosting classifier for its ability to handle class imbalance for binary classification between Chagas and non-Chagas cases.\n Main results.<\/jats:italic>\n The proposed framework was evaluated on the PhysioNet\/Computing in Cardiology (CinC) Challenge 2025 dataset. Evaluation on the hidden test data set yielded an accuracy of 90.53\n \n \n \n <\/jats:tex-math>\n \n \n %<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n F<\/jats:italic>\n 1 Chagas = 10.73\n \n \n \n <\/jats:tex-math>\n \n \n %<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , AUROC = 63.67\n \n \n \n <\/jats:tex-math>\n \n \n %<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , AUPRC = 11.96\n \n \n \n <\/jats:tex-math>\n \n \n %<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and Challenge Score = 20.5\n \n \n \n <\/jats:tex-math>\n \n \n %<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n under the team name Medics.\n Significance.<\/jats:italic>\n The findings of this work highlight the potential of signal processing and machine learning-based analysis of ECG for scalable, non-invasive, and cost-effective early detection of Chagas disease, supporting improved clinical decision-making and preventive healthcare strategies.\n <\/jats:p>","DOI":"10.1088\/1361-6579\/ae4b82","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T22:53:23Z","timestamp":1772232803000},"page":"035008","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated detection of Chagas disease from ECG signals using wavelet scattering transform and RUSBoost classifier"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0306-5224","authenticated-orcid":true,"given":"Shivnarayan","family":"Patidar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"pmeaae4b82bib1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.echo.2017.10.019","type":"journal-article","article-title":"Recommendations for multimodality cardiac imaging in Chagas disease: a report from the American Society of Echocardiography","volume":"31","author":"Acquatella","year":"2018","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"pmeaae4b82bib2","doi-asserted-by":"publisher","first-page":"4114","DOI":"10.1109\/TSP.2014.2326991","type":"journal-article","article-title":"Deep scattering spectrum","volume":"62","author":"And\u00e9n","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"pmeaae4b82bib3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pntd.0009974","type":"journal-article","article-title":"Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients\u2013the SaMi-Trop cohort","volume":"15","author":"Brito","year":"2021","journal-title":"PLoS Negl. Trop. Dis."},{"key":"pmeaae4b82bib4","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-011181","type":"journal-article","article-title":"Longitudinal study of patients with chronic Chagas cardiomyopathy in Brazil (SaMi-Trop project): a cohort profile","volume":"6","author":"Cardoso","year":"2016","journal-title":"BMJ Open"},{"key":"pmeaae4b82bib5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2022.101587","type":"journal-article","article-title":"A convolutional neural network to recognize Chagas disease vectors using mobile phone images","volume":"68","author":"Cochero","year":"2022","journal-title":"Ecol. Inform."},{"key":"pmeaae4b82bib6","first-page":"1","type":"conference-proceedings","article-title":"Deep learning and permutation entropy in the stratification of patients with Chagas disease","volume":"vol 498","author":"Cornejo","year":"2022"},{"key":"pmeaae4b82bib7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22489\/CinC.2020.270","type":"conference-proceedings","article-title":"Application of permutation entropy in the stratification of patients with Chagas disease","author":"Cornejo","year":"2020"},{"key":"pmeaae4b82bib8","doi-asserted-by":"publisher","DOI":"10.1016\/j.lana.2024.100881","type":"journal-article","article-title":"The epidemiology of Chagas disease in the Americas","volume":"37","author":"Cucunub\u00e1","year":"2024","journal-title":"Lancet Reg. Health Am."},{"key":"pmeaae4b82bib9","doi-asserted-by":"publisher","DOI":"10.33448\/rsd-v10i4.13879","type":"journal-article","article-title":"Machine learning and automatic selection of attributes for the identification of Chagas disease from clinical and sociodemographic data","volume":"10","author":"de Santana Teles","year":"2021","journal-title":"Res. Soc. Dev."},{"key":"pmeaae4b82bib10","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3390\/informatics8040079","type":"journal-article","article-title":"Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis","volume":"8","author":"Elgeldawi","year":"2021","journal-title":"Informatics"},{"key":"pmeaae4b82bib11","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","type":"journal-article","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"pmeaae4b82bib12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pntd.0012026","type":"journal-article","article-title":"Machine learning for predicting Chagas disease infection in rural areas of Brazil","volume":"18","author":"Ghilardi","year":"2024","journal-title":"PLoS Negl. Trop. Dis."},{"key":"pmeaae4b82bib13","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","type":"journal-article","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":"pmeaae4b82bib14","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","type":"journal-article","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"pmeaae4b82bib15","doi-asserted-by":"publisher","first-page":"785","DOI":"10.3390\/electronics11050785","type":"journal-article","article-title":"Machine learning-based feature selection and classification for the experimental diagnosis of Trypanosoma cruzi","volume":"11","author":"Hevia-Montiel","year":"2022","journal-title":"Electronics"},{"key":"pmeaae4b82bib16","first-page":"1","type":"conference-proceedings","article-title":"Managing label uncertainty in the detection of Chagas disease from the ECG","volume":"vol 52","author":"Hong","year":"2025"},{"key":"pmeaae4b82bib17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0011118","type":"journal-article","article-title":"Screening for Chagas disease from the electrocardiogram using a deep neural network","volume":"17","author":"Jidling","year":"2023","journal-title":"PLoS Negl. Trop. Dis."},{"key":"pmeaae4b82bib18","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1002\/cpa.21413","type":"journal-article","article-title":"Group invariant scattering","volume":"65","author":"Mallat","year":"2011","journal-title":"Commun. Pure Appl. Math."},{"key":"pmeaae4b82bib19","first-page":"1","type":"conference-proceedings","article-title":"Knowledge distillation from general ECG classification model for Chagas disease detection in 12-lead ECGs","volume":"vol 52","author":"Nejedly","year":"2025"},{"key":"pmeaae4b82bib20","first-page":"1","type":"conference-proceedings","article-title":"Transformer\u2013xlstm ensembles for ECG-based Chagas disease detection","volume":"vol 52","author":"Nicolson","year":"2025"},{"key":"pmeaae4b82bib21","doi-asserted-by":"publisher","first-page":"e169","DOI":"10.1161\/CIR.0000000000000599","type":"journal-article","article-title":"Chagas cardiomyopathy: an update of current clinical knowledge and management: a scientific statement from the American Heart Association","volume":"138","author":"(On behalf of the American Heart Association Rheumatic Fever, Endocarditis and Kawasaki Disease Committee of the Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; and Stroke Council)","year":"2018","journal-title":"Circulation"},{"key":"pmeaae4b82bib22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJBIC.2022.120749","type":"journal-article","article-title":"Deep convolutional neural network applied to Trypanosoma Cruzi detection in blood samples","volume":"19","author":"Pereira","year":"2022","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"pmeaae4b82bib23","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1016\/S0140-6736(10)60061-X","type":"journal-article","article-title":"Chagas disease","volume":"375","author":"Rassi","year":"2010","journal-title":"Lancet"},{"key":"pmeaae4b82bib24","article-title":"Detection of Chagas disease from the ECG: the George B. Moody PhysioNet Challenge 2025","author":"Reyna","year":"2025a","type":"preprint"},{"key":"pmeaae4b82bib25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000324","type":"journal-article","article-title":"Heart murmur detection from phonocardiogram recordings: the George B. Moody PhysioNet Challenge 2022","volume":"2","author":"Reyna","year":"2023","journal-title":"PLoS Digit. Health"},{"key":"pmeaae4b82bib26","first-page":"1","type":"conference-proceedings","article-title":"Detection of Chagas disease from the ECG: the George B. Moody PhysioNet Challenge 2025","volume":"vol 52","author":"Reyna","year":"2025b"},{"key":"pmeaae4b82bib27","doi-asserted-by":"publisher","first-page":"1760","DOI":"10.1038\/s41467-020-15432-4","type":"journal-article","article-title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","volume":"11","author":"Ribeiro","year":"2020","journal-title":"Nat. Commun."},{"key":"pmeaae4b82bib28","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1038\/nrcardio.2012.109","type":"journal-article","article-title":"Diagnosis and management of Chagas disease and cardiomyopathy","volume":"9","author":"Ribeiro","year":"2012","journal-title":"Nat. Rev. Cardiol."},{"key":"pmeaae4b82bib29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22489\/CinC.2022.313","type":"conference-proceedings","article-title":"Approximate entropy and densely connected neural network in the early diagnostic of patients with Chagas disease","volume":"vol 49","author":"Rodr\u00edguez","year":"2022"},{"key":"pmeaae4b82bib30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22489\/CinC.2023.143","type":"conference-proceedings","article-title":"Optimal artificial neural network for the diagnosis of Chagas disease using approximate entropy and data augmentation","volume":"vol 50","author":"Rodriguez","year":"2023"},{"key":"pmeaae4b82bib31","doi-asserted-by":"publisher","first-page":"2732","DOI":"10.1109\/EMBC46164.2021.9629563","type":"conference-proceedings","article-title":"Convolutional neural networks for Chagas\u2019 parasite detection in histopathological images","author":"Sanchez-Pati\u00f1o","year":"2021"},{"key":"pmeaae4b82bib32","first-page":"1","type":"conference-proceedings","article-title":"Detecting Chagas disease using a vision transformer-based ECG foundation model","volume":"vol 52","author":"Santvliet","year":"2025"},{"key":"pmeaae4b82bib33","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TSMCA.2009.2029559","type":"journal-article","article-title":"RUSBoost: a hybrid approach to alleviating class imbalance","volume":"40","author":"Seiffert","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. A"},{"key":"pmeaae4b82bib34","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102513","type":"journal-article","article-title":"Prediction of echocardiographic parameters in Chagas disease using heart rate variability and machine learning","volume":"67","author":"Silva","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"pmeaae4b82bib35","first-page":"1","type":"conference-proceedings","article-title":"Biomarker-based pretraining for Chagas disease screening in electrocardiograms","volume":"vol 52","author":"Stenhede","year":"2025"},{"key":"pmeaae4b82bib36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22489\/CinC.2023.273","type":"conference-proceedings","article-title":"Chagas disease: an analysis with temporal features extraction, permutation entropy and a stratification of heart risk by a deep learning model","volume":"vol 50","author":"Valdez","year":"2023"},{"key":"pmeaae4b82bib37","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/ab03f7","type":"journal-article","article-title":"Nonlinear characterization of ECGs in patients with Chagas\u2019 disease","volume":"5","author":"Vizcardo","year":"2019a","journal-title":"Biomed. Phys. Eng. Express"},{"key":"pmeaae4b82bib38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22489\/CinC.2018.234","type":"conference-proceedings","article-title":"Use of approximation entropy for stratification of risk in patients with Chagas disease","volume":"vol 45","author":"Vizcardo","year":"2018"},{"key":"pmeaae4b82bib39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22489\/CinC.2019.112","type":"conference-proceedings","article-title":"Application of the entropy of approximation for the nonlinear characterization in patients with Chagas disease","author":"Vizcardo","year":"2019b"},{"key":"pmeaae4b82bib40","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1038\/s41597-020-0495-6","type":"journal-article","article-title":"PTB-XL, a large publicly available electrocardiography dataset","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci. Data"},{"key":"pmeaae4b82bib41","article-title":"Integrating neglected tropical diseases into global health and development: fourth who report on neglected tropical diseases","author":"World Health Organization","year":"2017","type":"other"}],"container-title":["Physiological Measurement"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T08:50:50Z","timestamp":1773219050000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ae4b82"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,11]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3,11]]},"published-print":{"date-parts":[[2026,3,31]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6579\/ae4b82","relation":{},"ISSN":["0967-3334","1361-6579"],"issn-type":[{"value":"0967-3334","type":"print"},{"value":"1361-6579","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,11]]},"assertion":[{"value":"Automated detection of Chagas disease from ECG signals using wavelet scattering transform and RUSBoost classifier","name":"article_title","label":"Article Title"},{"value":"Physiological Measurement","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-11-13","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-02-27","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}