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Detecting QT prolongation from a single-lead ECG with deep learning.

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Bibliographic Details
Title: Detecting QT prolongation from a single-lead ECG with deep learning.
Authors: Alam R; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America., Aguirre A; Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.; Harvard Medical School, Boston, Massachusetts, United States of America.; Harvard-MIT Program in Health Sciences and Technology, Cambridge, Massachusetts, United States of America., Stultz CM; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.; Harvard-MIT Program in Health Sciences and Technology, Cambridge, Massachusetts, United States of America.; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Source: PLOS digital health [PLOS Digit Health] 2024 Jun 25; Vol. 3 (6), pp. e0000539. Date of Electronic Publication: 2024 Jun 25 (Print Publication: 2024).
Publication Type: Journal Article
Journal Info: Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2767-3170
DOI:10.1371/journal.pdig.0000539