{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T08:01:08Z","timestamp":1785139268957,"version":"3.55.0"},"reference-count":46,"publisher":"IOP Publishing","issue":"7","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["RS-2023-00208284"],"award-info":[{"award-number":["RS-2023-00208284"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2026,7,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Chagas disease remains a major public-health concern in endemic regions, and chronic cardiac involvement often manifests as conduction abnormalities detectable on standard 12-lead electrocardiograms (ECGs). Reliable automated screening remains challenging because of dataset heterogeneity and label uncertainty, particularly when combining strongly labeled cohorts with large weakly labeled repositories.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We propose a hybrid architecture that integrates a 1D ResNet encoder for local ECG morphology, a bidirectional GRU for long-range temporal context, and handcrafted physiological features and demographics through an auxiliary-conditioned cross-attention module. The auxiliary vector, comprising age, sex, and QRS\/conduction descriptors, is projected into a query token that selectively attends to deep sequential embeddings for feature-aware temporal aggregation. To exploit heterogeneous sources while reflecting source reliability, we further adopt a source-aware weighted binary cross-entropy objective.\n                    <jats:italic>Main results.<\/jats:italic>\n                    As team CAUETUMN in the PhysioNet\/Computing in Cardiology Challenge 2025, the framework achieved a score of 0.347 on the organizer-held REDS-II leaderboard-validation set during the official phase and 0.218 on the final hidden test set, ranking 17th among 41 eligible teams.\n                    <jats:italic>Significance.<\/jats:italic>\n                    These results suggest that conditioning detection on interpretable QRS and conduction descriptors supports a transparent and physiologically informed screening framework, while highlighting the difficulty of generalizing across heterogeneous cohorts.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae8b72","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T22:57:14Z","timestamp":1784156234000},"page":"075014","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Auxiliary-conditioned cross-attention with physiologically interpretable features for chagas disease detection from 12-lead ECGs"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8615-6245","authenticated-orcid":false,"given":"Hyuno","family":"Im","sequence":"first","affiliation":[{"name":"Chung-Ang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4214-5095","authenticated-orcid":false,"given":"Nahyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Chung-Ang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2391-1583","authenticated-orcid":false,"given":"Taeyoung","family":"Kang","sequence":"additional","affiliation":[{"name":"Chung-Ang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1821-6133","authenticated-orcid":false,"given":"Taehwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Chung-Ang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4745-9731","authenticated-orcid":false,"given":"Donggun","family":"Kim","sequence":"additional","affiliation":[{"name":"Korea University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7969-2363","authenticated-orcid":true,"given":"Dongjae","family":"Lee","sequence":"additional","affiliation":[{"name":"Korea University Guro Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4975-9977","authenticated-orcid":true,"given":"Seungsang","family":"Oh","sequence":"additional","affiliation":[{"name":"Korea University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3147-4028","authenticated-orcid":true,"given":"Wuming","family":"Gong","sequence":"additional","affiliation":[{"name":"University of Minnesota-Twin Cities"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7117-7669","authenticated-orcid":true,"given":"Il-Youp","family":"Kwak","sequence":"additional","affiliation":[{"name":"Chung-Ang University"},{"name":"Chung-Ang University"},{"name":"Chung-Ang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"pmeaae8b72bib1","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41591-018-0240-2","type":"journal-article","article-title":"Screening for cardiac contractile dysfunction using an artificial intelligence\u2013enabled electrocardiogram","volume":"25","author":"Attia","year":"2019","journal-title":"Nat. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-02-07","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-07-15","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-07-27","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}